research
∙
09/14/2023
Gradient constrained sharpness-aware prompt learning for vision-language models
This paper targets a novel trade-off problem in generalizable prompt lea...
share
research
∙
09/02/2023
Regularly Truncated M-estimators for Learning with Noisy Labels
The sample selection approach is very popular in learning with noisy lab...
research
∙
08/31/2023
Continual Learning From a Stream of APIs
Continual learning (CL) aims to learn new tasks without forgetting previ...
research
∙
08/26/2023
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Sample selection is a prevalent method in learning with noisy labels, wh...
research
∙
08/26/2023
Point-Query Quadtree for Crowd Counting, Localization, and More
We show that crowd counting can be viewed as a decomposable point queryi...
research
∙
08/22/2023
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the c...
research
∙
08/16/2023
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
Contrastive Language-Image Pre-training (CLIP) has significantly boosted...
research
∙
08/14/2023
Channel-Wise Contrastive Learning for Learning with Noisy Labels
In real-world datasets, noisy labels are pervasive. The challenge of lea...
research
∙
07/26/2023
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions
Relying on large-scale training data with pixel-level labels, previous e...
research
∙
07/12/2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Generating unlabeled data has been recently shown to help address the fe...
research
∙
07/11/2023
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
In recent years, research on learning with noisy labels has focused on d...
research
∙
07/03/2023
Why do CNNs excel at feature extraction? A mathematical explanation
Over the past decade deep learning has revolutionized the field of compu...
research
∙
06/30/2023
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer
Deep neural networks often suffer from poor generalization due to comple...
research
∙
06/22/2023
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
A pooling operation is essential for effective graph-level representatio...
research
∙
06/20/2023
A Universal Unbiased Method for Classification from Aggregate Observations
In conventional supervised classification, true labels are required for ...
research
∙
06/12/2023
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Training a classifier exploiting a huge amount of supervised data is exp...
research
∙
06/06/2023
Towards Label-free Scene Understanding by Vision Foundation Models
Vision foundation models such as Contrastive Vision-Language Pre-trainin...
research
∙
06/06/2023
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Out-of-distribution (OOD) detection is an indispensable aspect of secure...
research
∙
06/05/2023
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
Learning from crowds describes that the annotations of training data are...
research
∙
05/28/2023
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Label-noise learning (LNL) aims to increase the model's generalization g...
research
∙
05/18/2023
Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation
A number of deep models trained on high-quality and valuable images have...
research
∙
05/10/2023
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths
Quantized Neural Networks (QNNs) receive increasing attention in resourc...
research
∙
04/20/2023
Multiscale Representation for Real-Time Anti-Aliasing Neural Rendering
The rendering scheme in neural radiance field (NeRF) is effective in ren...
research
∙
04/12/2023
Unicom: Universal and Compact Representation Learning for Image Retrieval
Modern image retrieval methods typically rely on fine-tuning pre-trained...
research
∙
03/23/2023
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
Robust generalization aims to tackle the most challenging data distribut...
research
∙
03/22/2023
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency
As one of the most fundamental techniques in multimodal learning, cross-...
research
∙
03/21/2023
Dynamic-Aware Loss for Learning with Label Noise
Label noise poses a serious threat to deep neural networks (DNNs). Emplo...
research
∙
03/20/2023
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
The goal of data-free meta-learning is to learn useful prior knowledge f...
research
∙
03/09/2023
Out-of-distribution Detection with Implicit Outlier Transformation
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection...
research
∙
03/04/2023
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels h...
research
∙
03/01/2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Privacy and security concerns in real-world applications have led to the...
research
∙
02/07/2023
HumanMAC: Masked Motion Completion for Human Motion Prediction
Human motion prediction is a classical problem in computer vision and co...
research
∙
12/07/2022
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
Convolutional Neural Networks (CNNs) have demonstrated superiority in le...
research
∙
11/01/2022
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Adversarial training (AT) with imperfect supervision is significant but ...
research
∙
10/04/2022
Strength-Adaptive Adversarial Training
Adversarial training (AT) is proved to reliably improve network's robust...
research
∙
09/29/2022
Towards Lightweight Black-Box Attacks against Deep Neural Networks
Black-box attacks can generate adversarial examples without accessing th...
research
∙
08/30/2022
Symmetric Pruning in Quantum Neural Networks
Many fundamental properties of a quantum system are captured by its Hami...
research
∙
07/25/2022
Improving Adversarial Robustness via Mutual Information Estimation
Deep neural networks (DNNs) are found to be vulnerable to adversarial no...
research
∙
07/07/2022
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Machine learning models are vulnerable to Out-Of-Distribution (OOD) exam...
research
∙
06/17/2022
Understanding Robust Overfitting of Adversarial Training and Beyond
Robust overfitting widely exists in adversarial training of deep network...
research
∙
06/16/2022
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos
Multimodal sentiment analysis in videos is a key task in many real-world...
research
∙
06/11/2022
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Through using only a well-trained classifier, model-inversion (MI) attac...
research
∙
06/07/2022
Recent Advances for Quantum Neural Networks in Generative Learning
Quantum computers are next-generation devices that hold promise to perfo...
research
∙
06/06/2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
In label-noise learning, estimating the transition matrix has attracted ...
research
∙
06/04/2022
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
Most recent self-supervised learning methods learn visual representation...
research
∙
05/30/2022
Robust Weight Perturbation for Adversarial Training
Overfitting widely exists in adversarial robust training of deep network...
research
∙
05/27/2022
Counterfactual Fairness with Partially Known Causal Graph
Fair machine learning aims to avoid treating individuals or sub-populati...
research
∙
05/27/2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
State-of-the-art causal discovery methods usually assume that the observ...
research
∙
05/18/2022
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Pluralistic image completion focuses on generating both visually realist...
research
∙
04/15/2022
