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Found 61 papers, 36 papers with code
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection
We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns.
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework
To tackle this challenge, methodically we propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC) and build our optimization framework on top of it.
Towards Demystifying the Generalization Behaviors When Neural Collapse Emerges
While there have been extensive studies on optimization characteristics showing the global optimality of neural collapse, little research has been done on the generalization behaviors during the occurrence of NC.
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
Ranked #6 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
Learning node representation via Motif Coarsening
For the motif-based node representation learning process, we propose a Motif Coarsening strategy for incorporating motif structure into the graph representation learning process.
AUC-Oriented Domain Adaptation: From Theory to Algorithm
We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function.
Revisiting AUC-oriented Adversarial Training with Loss-Agnostic Perturbations
On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods.
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning
However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$.
A Study of Neural Collapse Phenomenon: Grassmannian Frame, Symmetry and Generalization
Out of curiosity about the symmetry of Grassmannian Frame, we conduct experiments to explore if models with different Grassmannian Frames have different performance.
Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild
Finally, experimental results support the effectiveness of the proposed framework in terms of both mural synthesis and restoration.
Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective
Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones.
OpenAUC: Towards AUC-Oriented Open-Set Recognition
In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction.
Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm
sample size and a slow convergence rate, especially for TPAUC.
Recurrent Meta-Learning against Generalized Cold-start Problem in CTR Prediction
To attack this problem, we propose a recursive meta-learning model with the user's behavior sequence prediction as a separate training task.
A Unified Framework against Topology and Class Imbalance
We develop a multi-class AUC optimization work to deal with the class imbalance problem.
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering.
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning.
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).
A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game
From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction
In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection.
ER: Equivariance Regularizer for Knowledge Graph Completion
To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information.
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem.
Geometry Interaction Knowledge Graph Embeddings
Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks.
Optimizing Two-way Partial AUC with an End-to-end Framework
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling
However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error.
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Conditional image generation is an active research topic including text2image and image translation.
Conditional Image Generation
Generative Adversarial Network
+1
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Learning Meta-path-aware Embeddings for Recommender Systems
Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years.
Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions
As the core of the framework, the iterative relabeling module exploits the self-training principle to dynamically generate pseudo labels for user preferences.
Pareto Optimality for Fairness-constrained Collaborative Filtering
To balance overall recommendation performance and fairness, prevalent solutions apply fairness constraints or regularizations to enforce equality of certain performance across different subgroups.
Learning with Multiclass AUC: Theory and Algorithms
Our foundation is based on the M metric, which is a well-known multiclass extension of AUC.
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC.
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Poisoning Attack against Estimating from Pairwise Comparisons
In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems.
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered.
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones.
Ranked #8 on
RGB-D Salient Object Detection
on NJU2K
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction
To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL).
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection
There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data; (2) how to prevent the contamination effect from the unreliable depth map.
Ranked #22 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
Global Context-Aware Progressive Aggregation Network for Salient Object Detection
Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role.
Ranked #20 on
Dichotomous Image Segmentation
on DIS-TE1
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.
DM2C: Deep Mixed-Modal Clustering
Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency.
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer
Different from most of the previous work, pursuing the Block-Diagonal structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed.
Collaborative Preference Embedding against Sparse Labels
From the margin theory point-of-view, we then propose a generalization enhancement scheme for sparse and insufficient labels via optimizing the margin distribution.
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective.
Learning Personalized Attribute Preference via Multi-task AUC Optimization
Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators.
Deep Robust Subjective Visual Property Prediction in Crowdsourcing
The problem of estimating subjective visual properties (SVP) of images (e. g., Shoes A is more comfortable than B) is gaining rising attention.
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin
To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}).
Robust Ordinal Embedding from Contaminated Relative Comparisons
Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data.
A Margin-based MLE for Crowdsourced Partial Ranking
A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order.
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments.
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes.
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains.
Exploring Outliers in Crowdsourced Ranking for QoE
Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years.
Parsimonious Mixed-Effects HodgeRank for Crowdsourced Preference Aggregation
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or utility function which generates their comparison behaviors in experiments.
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
With the rapid growth of crowdsourcing platforms it has become easy and relatively inexpensive to collect a dataset labeled by multiple annotators in a short time.
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
In this setting, a pairwise comparison dataset is typically gathered via random sampling, either \emph{with} or \emph{without} replacement.
Evaluating Visual Properties via Robust HodgeRank
In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms.
