Search Results for author:
Found 144 papers, 49 papers with code
Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces
In this paper, we relax the intra-class constraint of ArcFace to improve the robustness to label noise.
LTF: A Label Transformation Framework for Correcting Label Shift
Distribution shift is a major obstacle to the deployment of current deep learning models on real-world problems.
Label-Noise Robust Domain Adaptation
Domain adaptation aims to correct the classifiers when faced with distribution shift between source (training) and target (test) domains.
Hyper3D: Efficient 3D Representation via Hybrid Triplane and Octree Feature for Enhanced 3D Shape Variational Auto-Encoders
Existing 3D shape VAEs often employ uniform point sampling and 1D/2D latent representations, such as vector sets or triplanes, leading to significant geometric detail loss due to inadequate surface coverage and the absence of explicit 3D representations in the latent space.
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
The remarkable achievements of large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.
MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input
To address this, we propose a Mask-Free VITON (MF-VITON) framework that achieves realistic VITON using only a single person image and a target garment, eliminating the requirement for auxiliary masks.
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain parameter-efficient fine-tuning method based on inverse Discrete Cosine Transform (iDCT) with selective locations of learnable components.
Safety at Scale: A Comprehensive Survey of Large Model Safety
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI).
A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confounding
Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics.
Admitting Ignorance Helps the Video Question Answering Models to Answer
We argue that these models often establish shortcuts, resulting in spurious correlations between questions and answers, especially when the alignment between video and text data is suboptimal.
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
For the first time, it achieves panoptic 3D reconstruction of open-world environments directly from the RGB-D video.
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
We first construct a large-scale 3D scene dataset based on 3DGS, dubbed \textbf{SegGaussian}, which provides detailed semantic and instance annotations for both Gaussian points and multi-view images.
UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation
Recently, text-to-image generation models have achieved remarkable advancements, particularly with diffusion models facilitating high-quality image synthesis from textual descriptions.
Uncertainty Quantification in Stereo Matching
Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability.
SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training
For the first time, our model SnapGen, demonstrates the generation of 1024x1024 px images on a mobile device around 1. 4 seconds.
Urban4D: Semantic-Guided 4D Gaussian Splatting for Urban Scene Reconstruction
Reconstructing dynamic urban scenes presents significant challenges due to their intrinsic geometric structures and spatiotemporal dynamics.
Revisit Non-parametric Two-sample Testing as a Semi-supervised Learning Problem
Learning effective data representations is crucial in answering if two samples X and Y are from the same distribution (a. k. a.
TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On
Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust generative capabilities of text-to-image (T2I) diffusion backbones.
LaVin-DiT: Large Vision Diffusion Transformer
Second, for generative modeling, we develop a joint diffusion transformer that progressively produces vision outputs.
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Subsequently, we extend this analysis to encompass scenarios where hidden confounders exhibit causal dependencies, with the causal structure of latent variables described by a Directed Acyclic Graph (DAG).
Efficient Neural Implicit Representation for 3D Human Reconstruction
Our models achieve a training speed that is 110X faster than that of State-of-The-Art (SoTA) NeRF-based models.
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery.
Rethinking State Disentanglement in Causal Reinforcement Learning
We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.
CT4D: Consistent Text-to-4D Generation with Animatable Meshes
The primary challenges of our mesh-based framework involve stably generating a mesh with details that align with the text prompt while directly driving it and maintaining surface continuity.
Optimal Kernel Choice for Score Function-based Causal Discovery
In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data.
AniFaceDiff: Animating Stylized Avatars via Parametric Conditioned Diffusion Models
Animating stylized avatars with dynamic poses and expressions has attracted increasing attention for its broad range of applications.
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored.
Self-Distilled Disentangled Learning for Counterfactual Prediction
The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, confounders, and adjustable variables.
Training-Free Robust Interactive Video Object Segmentation
In this paper, we propose a training-free prompt tracking framework for interactive video object segmentation (I-PT), leveraging the powerful generalization of SAM.
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Although we expect such instantaneous dependence has consistency with the true causal relation in certain sense to make the discovery results meaningful, it remains unclear what type of consistency we need and when will such consistency be satisfied.
On the Identification of Temporally Causal Representation with Instantaneous Dependence
To fill this gap, we propose an \textbf{ID}entification framework for instantane\textbf{O}us \textbf{L}atent dynamics (\textbf{IDOL}) by imposing a sparse influence constraint that the latent causal processes have sparse time-delayed and instantaneous relations.
STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario
Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness.
Identifiable Latent Neural Causal Models
This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models
It can represent that the contrastive learning between the visual holistic representations and the multimodal fine-grained features of document objects can assist the vision encoder in acquiring more effective visual cues, thereby enhancing the comprehension of text-rich documents in LVLMs.
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
While existing weakly-supervised approaches leverage image-text pairs to reduce the expansive annotation cost, the lack of mask supervision makes it difficult for the model to locate multiple instances and accurately group pixels with similar semantics, significantly hampering versatility and performance.
Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models
Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.
Discovery of the Hidden World with Large Language Models
Meanwhile, COAT also adopts CDs to find causal relations among the identified variables as well as to provide feedback to LLMs to iteratively refine the proposed factors.
GraphRT: A graph-based deep learning model for predicting the retention time of peptides
GraphRT is a graph based deep learning model that predicts the retention time (RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS) experiments.
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
To address this limitation, this paper proposes a framework for achieving causal fairness based on the notion of interventions when the true causal graph is partially known.
Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness
To tackle these issues, we introduce FreeTalker, which, to the best of our knowledge, is the first framework for the generation of both spontaneous (e. g., co-speech gesture) and non-spontaneous (e. g., moving around the podium) speaker motions.
Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering
While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling have been largely overlooked in existing research.
HuTuMotion: Human-Tuned Navigation of Latent Motion Diffusion Models with Minimal Feedback
We introduce HuTuMotion, an innovative approach for generating natural human motions that navigates latent motion diffusion models by leveraging few-shot human feedback.
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
Transformers have been recently explored for 3D point cloud understanding with impressive progress achieved.
Ranked #7 on
Semantic Segmentation
on S3DIS Area5
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
In this paper, we present conditions for identifying the generator of a linear stochastic differential equation (SDE) from the distribution of its solution process with a given fixed initial state.
Identifiable Latent Polynomial Causal Models Through the Lens of Change
However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.
Causal-learn: Causal Discovery in Python
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC).
An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection
Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively.
CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer
In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler).
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering
Our approach includes a density Mip-VoG for scene geometry and a feature Mip-VoG with a small MLP for view-dependent color.
Knowledge Distillation for Feature Extraction in Underwater VSLAM
This paper proposes a cross-modal knowledge distillation framework for training an underwater feature detection and matching network (UFEN).
Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation
Recently, Table Structure Recognition (TSR) task, aiming at identifying table structure into machine readable formats, has received increasing interest in the community.
Mosaic Representation Learning for Self-supervised Visual Pre-training
To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations.
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
As selected data have high discrepancies in probabilities, the divergence of two networks can be maintained by training on such data.
Generating Dynamic Kernels via Transformers for Lane Detection
While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies.
Ranked #1 on
Lane Detection
on CurveLanes
Unpaired Image-to-Image Translation With Shortest Path Regularization
In this paper, we start from a different perspective and consider the paths connecting the two domains.
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.
3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue
However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows.
Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis
To explore the role of the relation between edges, this paper proposes a novel Adaptive Edge-to-Edge Interaction Learning module, which aims to enhance the point-to-point relation through modelling the edge-to-edge interaction in the local region adaptively.
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory.
Strength-Adaptive Adversarial Training
Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum.
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions.
On the Sparse DAG Structure Learning Based on Adaptive Lasso
Learning the underlying Bayesian Networks (BNs), represented by directed acyclic graphs (DAGs), of the concerned events from purely-observational data is a crucial part of evidential reasoning.
Truncated Matrix Power Iteration for Differentiable DAG Learning
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem.
Identifying Weight-Variant Latent Causal Models
The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.
Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift
Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.
MetaComp: Learning to Adapt for Online Depth Completion
However, facing a new environment where the test data occurs online and differs from the training data in the RGB image content and depth sparsity, the trained model might suffer severe performance drop.
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption
Specifically, by carefully examining the spatial and temporal properties, we propose to disrupt a real video through a Pseudo-fake Generator and create a wide range of pseudo-fake videos for training.
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention.
Style Interleaved Learning for Generalizable Person Re-identification
This common practice causes the model to overfit to existing feature styles in the source domain, resulting in sub-optimal generalization ability on target domains.
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models.
Understanding Robust Overfitting of Adversarial Training and Beyond
Here, we explore the causes of robust overfitting by comparing the data distribution of \emph{non-overfit} (weak adversary) and \emph{overfitted} (strong adversary) adversarial training, and observe that the distribution of the adversarial data generated by weak adversary mainly contain small-loss data.
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
However, because environments are not labelled, the extracted information inevitably contains redundant information unrelated to the dynamics in transition segments and thus fails to maintain a crucial property of $Z$: $Z$ should be similar in the same environment and dissimilar in different ones.
Robust Weight Perturbation for Adversarial Training
Based on these observations, we propose a robust perturbation strategy to constrain the extent of weight perturbation.
Counterfactual Fairness with Partially Known Causal Graph
Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph.
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations.
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Instead of explicitly disentangling global or component-wise modeling, the cross-attention mechanism can attend to the right local styles in the reference glyphs and aggregate the reference styles into a fine-grained style representation for the given content glyphs.
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
We show that in causal learning, the excess risk depends on the size of the source sample at a rate of $O(\frac{1}{m})$ only if the labelling distribution between the source and target domains remains unchanged.
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
The first one lets T compete with G to achieve maximum perturbation.
Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels.
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data.
FedDAG: Federated DAG Structure Learning
To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server.
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?
We provide reasoning why when the supports of the source and target data from overlap, any map of $X$ that is fixed across domains may not be suitable for domain adaptation via invariant features.
CRIS: CLIP-Driven Referring Image Segmentation
In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances.
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution.
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?
Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix.
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples
The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training.
Unaligned Image-to-Image Translation by Learning to Reweight
An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e. g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain.
Instance-dependent Label-noise Learning under a Structural Causal Model
In particular, we show that properly modeling the instances will contribute to the identifiability of the label noise transition matrix and thus lead to a better classifier.
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501.
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
In this paper, we propose a weakly supervised do-main adaptation setting, in which we can partially label newdatasets with bounding boxes, which are easier and cheaperto obtain than segmentation masks.
Exploring Set Similarity for Dense Self-supervised Representation Learning
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks.
Kernel Mean Estimation by Marginalized Corrupted Distributions
Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms.
CausalAdv: Adversarial Robustness through the Lens of Causality
The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning.
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try.
Ranked #28 on
Image Classification
on mini WebVision 1.0
Instance Correction for Learning with Open-set Noisy Labels
Lots of approaches, e. g., loss correction and label correction, cannot handle such open-set noisy labels well, since they need training data and test data to share the same label space, which does not hold for learning with open-set noisy labels.
Learning with Group Noise
Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels.
Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation
Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.
Improving robustness of softmax corss-entropy loss via inference information
Several state-of-the-arts start from improving the inter-class separability of training samples by modifying loss functions, where we argue that the adversarial samples are ignored and thus limited robustness to adversarial attacks is resulted.
Contextual Graph Reasoning Networks
Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks.
Score-based Causal Discovery from Heterogeneous Data
Most algorithms in causal discovery consider a single domain with a fixed distribution.
Not All Operations Contribute Equally: Hierarchical Operation-Adaptive Predictor for Neural Architecture Search
Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures.
Domain Generalization via Entropy Regularization
To arrive at this, some methods introduce a domain discriminator through adversarial learning to match the feature distributions in multiple source domains.
Ranked #52 on
Domain Generalization
on PACS
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database.
Deep Learning is Singular, and That's Good
In singular models, the optimal set of parameters forms an analytic set with singularities and classical statistical inference cannot be applied to such models.
Class2Simi: A New Perspective on Learning with Label Noise
It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise.
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
3D Object Reconstruction From A Single Image
3D Shape Retrieval
+4
Short-Term and Long-Term Context Aggregation Network for Video Inpainting
Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal.
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Furthermore, considering the mutli-modality of input data, we exploit the graph propagation on the two modalities respectively to extract multi-modal representations.
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image.
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices.
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not.
Multi-Class Classification from Noisy-Similarity-Labeled Data
We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances.
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of the positive data distribution cannot be contained in the support of the negative data distribution.
Domain Adaptation as a Problem of Inference on Graphical Models
Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain.
Twin Auxilary Classifiers GAN
One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.
Ranked #2 on
Image Generation
on CIFAR-100
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.
Learning Multi-level Weight-centric Features for Few-shot Learning
Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning.
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades.
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Due to the stylish difference between synthetic and real images, we propose a temporally-consistent domain adaptation (TCDA) approach that simultaneously explores labels in the synthetic domain and temporal constraints in the videos to improve style transfer and depth prediction.
Twin Auxiliary Classifiers GAN
One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.
Ranked #3 on
Conditional Image Generation
on CIFAR-100
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments.
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures.
Ranked #76 on
Monocular Depth Estimation
on KITTI Eigen split
Generative-Discriminative Complementary Learning
The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases.
Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2].
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples.
Correcting the Triplet Selection Bias for Triplet Loss
Considering that the number of triplets grows cubically with the size of training data, triplet mining is thus indispensable for efficiently training with triplet loss.
Deep Domain Generalization via Conditional Invariant Adversarial Networks
Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.
Ranked #76 on
Domain Generalization
on PACS
Domain Generalization via Conditional Invariant Representation
With the conditional invariant representation, the invariance of the joint distribution $\mathbb{P}(h(X), Y)$ can be guaranteed if the class prior $\mathbb{P}(Y)$ does not change across training and test domains.
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Our model consists of three mutually dependent modules which regulate each other: (1) a discriminative network that learns a fixed-length representation from local features and maps them to disease severity; (2) an attention mechanism that provides interpretability by focusing on the areas of the anatomy that contribute the most to the prediction task; and (3) a generative network that encourages the diversity of the local latent features.
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
In the proposed networks, different levels of features at each spatial location are adaptively re-weighted according to the local structure and surrounding contextual information before aggregation.
Deep Ordinal Regression Network for Monocular Depth Estimation
These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions.
Ranked #13 on
Depth Estimation
on NYU-Depth V2
An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption
In this paper, we study the mixture proportion estimation (MPE) problem in a new setting: given samples from the mixture and the component distributions, we identify the proportions of the components in the mixture distribution.
Causal Generative Domain Adaptation Networks
For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains.
Learning with Biased Complementary Labels
We therefore reason that the transition probabilities will be different.
A Coarse-Fine Network for Keypoint Localization
To target this problem, we develop a keypoint localization network composed of several coarse detector branches, each of which is built on top of a feature layer in a CNN, and a fine detector branch built on top of multiple feature layers.
A Compromise Principle in Deep Monocular Depth Estimation
However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions.
Transfer Learning with Label Noise
However, when learning this invariant knowledge, existing methods assume that the labels in source domain are uncontaminated, while in reality, we often have access to source data with noisy labels.
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally.
