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Found 130 papers, 77 papers with code
Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning
To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model.
Simple Graph Condensation
The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph.
On the Concept Trustworthiness in Concept Bottleneck Models
Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck.
Advantage-Aware Policy Optimization for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the Out-Of-Distribution (OOD) problem.
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation
To address these challenges, we have tailored a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework by effectively transferring cross-city knowledge for human trajectory simulation.
Training-Free Pretrained Model Merging
Furthermore, the visualization of the merged model within the multi-task loss landscape reveals that MuDSC enables the merged model to reside in the overlapping segment, featuring a unified lower loss for each task.
Improved Regret for Bandit Convex Optimization with Delayed Feedback
Previous studies have established a regret bound of $O(T^{3/4}+d^{1/3}T^{2/3})$ for this problem, where $d$ is the maximum delay, by simply feeding delayed loss values to the classical bandit gradient descent (BGD) algorithm.
Nearly Optimal Regret for Decentralized Online Convex Optimization
Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functions respectively, where $n$ is the number of local learners, $\rho<1$ is the spectral gap of the communication matrix, and $T$ is the time horizon.
Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios
Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.
Disentangled Condensation for Large-scale Graphs
Graph condensation has emerged as an intriguing technique to provide Graph Neural Networks for large-scale graphs with a more compact yet informative small graph to save the expensive costs of large-scale graph learning.
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise).
Pair-wise Layer Attention with Spatial Masking for Video Prediction
To this end, we present a Pair-wise Layer Attention with Spatial Masking (PLA-SM) framework for video prediction to capture the spatiotemporal dynamics, which reflect the motion trend.
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size.
ModelGiF: Gradient Fields for Model Functional Distance
The last decade has witnessed the success of deep learning and the surge of publicly released trained models, which necessitates the quantification of the model functional distance for various purposes.
Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket
Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large input graphs while preserving original performance.
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision.
MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling
In this paper, we propose MPM, a unified 2D-3D human pose representation framework via masked pose modeling.
Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation
The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.
Curricular Subgoals for Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning.
Decentralized SGD and Average-direction SAM are Asymptotically Equivalent
Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on massive devices simultaneously without the control of a central server.
Message-passing selection: Towards interpretable GNNs for graph classification
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.
Improved Projection-free Online Continuous Submodular Maximization
We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently.
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Non-stationary Online Convex Optimization with Arbitrary Delays
Despite its simplicity, our novel analysis shows that the dynamic regret of DOGD can be automatically bounded by $O(\sqrt{\bar{d}T}(P_T+1))$ under mild assumptions, and $O(\sqrt{dT}(P_T+1))$ in the worst case, where $\bar{d}$ and $d$ denote the average and maximum delay respectively, $T$ is the time horizon, and $P_T$ is the path length of comparators.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
The whole temporal neighborhood of nodes reveals the varying preferences of nodes.
Life Regression based Patch Slimming for Vision Transformers
In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go.
ViT-Calibrator: Decision Stream Calibration for Vision Transformer
In this work, we propose a new paradigm dubbed Decision Stream Calibration that boosts the performance of general Vision Transformers.
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning
This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.
Schema Inference for Interpretable Image Classification
In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema.
Model Doctor for Diagnosing and Treating Segmentation Error
Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries.
DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods.
Team DETR: Guide Queries as a Professional Team in Detection Transformers
Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance.
Improved Dynamic Regret for Online Frank-Wolfe
In this way, we first show that the dynamic regret bound of OFW can be improved to $O(\sqrt{T(1+V_T)})$ for smooth functions.
DepGraph: Towards Any Structural Pruning
Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks.
Recent advances in artificial intelligence for retrosynthesis
Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets.
A Loopback Network for Explainable Microvascular Invasion Classification
The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas.
How To Prevent the Continuous Damage of Noises To Model Training?
To address this problem, the proposed GSS alleviates the damage by switching the current gradient direction of each sample to a new direction selected from a gradient direction pool, which contains all-class gradient directions with different probabilities.
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.
SASFormer: Transformers for Sparsely Annotated Semantic Segmentation
In this work, we propose a simple yet effective sparse annotated semantic segmentation framework based on segformer, dubbed SASFormer, that achieves remarkable performance.
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels.
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges
In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods.
Attention Diversification for Domain Generalization
Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features.
A Survey of Neural Trees
This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability.
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
The global prototypes are adopted to provide the global view of objects to guide local prototypes to concentrate on the foreground while eliminating the influence of the background.
Re-Attention Transformer for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.
Federated Selective Aggregation for Knowledge Amalgamation
The goal of FedSA is to train a student model for a new task with the help of several decentralized teachers, whose pre-training tasks and data are different and agnostic.
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection
Anomaly detection aims at identifying deviant samples from the normal data distribution.
Learning Graph Neural Networks for Image Style Transfer
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
Learning with Recoverable Forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge.
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.
Topology-aware Generalization of Decentralized SGD
Our theory indicates that the generalizability of D-SGD is positively correlated with the spectral gap, and can explain why consensus control in initial training phase can ensure better generalization.
Label Matching Semi-Supervised Object Detection
To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.
Slimmable Domain Adaptation
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey
Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
Comparison Knowledge Translation for Generalizable Image Classification
In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories.
Spot-adaptive Knowledge Distillation
Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks.
CNN LEGO: Disassembling and Assembling Convolutional Neural Network
the feature maps are adopted to locate the critical features in each layer.
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.
Meta-attention for ViT-backed Continual Learning
Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks.
Knowledge Amalgamation for Object Detection with Transformers
Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
Imbalanced Sample Generation and Evaluation for Power System Transient Stability Using CTGAN
This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.
Up to 100$\times$ Faster Data-free Knowledge Distillation
At the heart of our approach is a novel strategy to reuse the shared common features in training data so as to synthesize different data instances.
Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers
The aggregate gradient strategy is a versatile module for mainstream CNN classifiers.
Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training
Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV).
A Survey of Deep Learning for Low-Shot Object Detection
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task.
Safe Distillation Box
In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Knowledge distillation~(KD) aims to craft a compact student model that imitates the behavior of a pre-trained teacher in a target domain.
Distribution Knowledge Embedding for Graph Pooling
A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs).
Track without Appearance: Learn Box and Tracklet Embedding with Local and Global Motion Patterns for Vehicle Tracking
In this paper, we try to explore the significance of motion patterns for vehicle tracking without appearance information.
Online Knowledge Distillation for Efficient Pose Estimation
However, existing pose distillation works rely on a heavy pre-trained estimator to perform knowledge transfer and require a complex two-stage learning procedure.
Boundary Knowledge Translation based Reference Semantic Segmentation
In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation.
Visual Boundary Knowledge Translation for Foreground Segmentation
To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.
Shape Controllable Virtual Try-on for Underwear Models
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
Amalgamating Knowledge From Heterogeneous Graph Neural Networks
In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs).
Tree-Like Decision Distillation
Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras
To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream.
Contrastive Model Inversion for Data-Free Knowledge Distillation
In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue.
KDExplainer: A Task-oriented Attention Model for Explaining Knowledge Distillation
Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs).
Training Generative Adversarial Networks in One Stage
Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort.
SPAGAN: Shortest Path Graph Attention Network
SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods.
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image
The existing two unsupervised methods are prone to failure on degenerated samples.
Self-Born Wiring for Neural Trees
Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter.
Progressive Network Grafting for Few-Shot Knowledge Distillation
In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category.
One-sample Guided Object Representation Disassembling
In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.
DEAL: Difficulty-aware Active Learning for Semantic Segmentation
To deal with this problem, we propose a semantic Difficulty-awarE Active Learning (DEAL) network composed of two branches: the common segmentation branch and the semantic difficulty branch.
Learning Propagation Rules for Attribution Map Generation
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map.
Factorizable Graph Convolutional Networks
In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.
Ranked #3 on
Node Classification
on PATTERN 100k
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer
Facial action unit (AU) intensity is an index to describe all visually discernible facial movements.
Impression Space from Deep Template Network
This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression.
Disassembling Object Representations without Labels
In this paper, we study a new representation-learning task, which we termed as disassembling object representations.
Distilling Knowledge from Graph Convolutional Networks
To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher.
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Then a dual generator is trained by taking the output from the former generator as input.
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.
Data-Free Adversarial Distillation
Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers
In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers.
Ranked #2 on
Lipreading
on CMLR
Dynamic Instance Normalization for Arbitrary Style Transfer
Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way.
Deep Model Transferability from Attribution Maps
Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter.
Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation
To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network.
A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading
When trained on CMLR dataset, the proposed CSSMCM surpasses the performance of state-of-the-art lip reading frameworks, which confirms the effectiveness of explicit modeling of tones for Chinese Mandarin lip reading.
Ranked #3 on
Lipreading
on CMLR
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations.
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
Many well-trained Convolutional Neural Network(CNN) models have now been released online by developers for the sake of effortless reproducing.
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
In this paper, we investigate a novel deep-model reusing task.
Amalgamating Knowledge towards Comprehensive Classification
We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}.
Selective Zero-Shot Classification with Augmented Attributes
We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes.
Interpretable Partitioned Embedding for Customized Fashion Outfit Composition
In the supervised attributes module, multiple attributes labels are adopted to ensure that different parts of the overall embedding correspond to different attributes.
Dual Swap Disentangling
To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.
Transductive Unbiased Embedding for Zero-Shot Learning
Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classes.
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control.
DeepSIC: Deep Semantic Image Compression
The same practice also enable the compressed code to carry the image semantic information during storage and transmission.
TripletGAN: Training Generative Model with Triplet Loss
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts.
Neural Style Transfer: A Review
We first propose a taxonomy of current algorithms in the field of NST.
Weakly Supervised Multiclass Video Segmentation
In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
Semi-Supervised Coupled Dictionary Learning for Person Re-identification
In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
Semi-supervised Node Splitting for Random Forest Construction
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.
Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation
Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.
Sparse Norm Filtering
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.
Spectral Graph Cut from a Filtering Point of View
In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.
