b'Tongliang Liu'

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...

0 Liangchen Liu, et al. ∙

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...

0 Xiaobo Xia, et al. ∙

research

∙ 08/31/2023

Continual Learning From a Stream of APIs

Continual learning (CL) aims to learn new tasks without forgetting previ...

0 Enneng Yang, et al. ∙

research

∙ 08/26/2023

Late Stopping: Avoiding Confidently Learning from Mislabeled Examples

Sample selection is a prevalent method in learning with noisy labels, wh...

0 Suqin Yuan, et al. ∙

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...

0 Chengxin Liu, et al. ∙

research

∙ 08/22/2023

ShadowNet for Data-Centric Quantum System Learning

Understanding the dynamics of large quantum systems is hindered by the c...

0 Yuxuan Du, et al. ∙

research

∙ 08/16/2023

ALIP: Adaptive Language-Image Pre-training with Synthetic Caption

Contrastive Language-Image Pre-training (CLIP) has significantly boosted...

0 Kaicheng Yang, et al. ∙

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...

0 Hui Kang, et al. ∙

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...

0 Wenjie Xuan, et al. ∙

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...

0 Ruijiang Dong, et al. ∙

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...

0 Hui Kang, et al. ∙

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...

0 Vinoth Nandakumar, et al. ∙

research

∙ 06/30/2023

Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer

Deep neural networks often suffer from poor generalization due to comple...

0 Peng Mi, et al. ∙

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...

0 Chuang Liu, et al. ∙

research

∙ 06/20/2023

A Universal Unbiased Method for Classification from Aggregate Observations

In conventional supervised classification, true labels are required for ...

0 Zixi Wei, et al. ∙

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...

0 Yuhao Wu, et al. ∙

research

∙ 06/06/2023

Towards Label-free Scene Understanding by Vision Foundation Models

Vision foundation models such as Contrastive Vision-Language Pre-trainin...

0 Runnan Chen, et al. ∙

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...

0 Jianing Zhu, et al. ∙

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...

0 Shikun Li, et al. ∙

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...

1 Jingfeng Zhang, et al. ∙

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...

0 Bochao Liu, et al. ∙

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...

0 Yulong Yang, et al. ∙

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...

0 Dongting Hu, et al. ∙

research

∙ 04/12/2023

Unicom: Universal and Compact Representation Learning for Image Retrieval

Modern image retrieval methods typically rely on fine-tuning pre-trained...

0 Xiang An, et al. ∙

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...

0 Zhuo Huang, et al. ∙

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-...

0 Shuo Yang, et al. ∙

research

∙ 03/21/2023

Dynamic-Aware Loss for Learning with Label Noise

Label noise poses a serious threat to deep neural networks (DNNs). Emplo...

0 Xiu-Chuan Li, et al. ∙

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...

0 Zixuan Hu, et al. ∙

research

∙ 03/09/2023

Out-of-distribution Detection with Implicit Outlier Transformation

Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection...

0 Qizhou Wang, et al. ∙

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...

0 Jiren Mai, et al. ∙

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...

0 Jianing Zhu, et al. ∙

research

∙ 02/07/2023

HumanMAC: Masked Motion Completion for Human Motion Prediction

Human motion prediction is a classical problem in computer vision and co...

0 Ling-Hao Chen, et al. ∙

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...

0 Huaxi Huang, et al. ∙

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 ...

1 Jianan Zhou, et al. ∙

research

∙ 10/04/2022

Strength-Adaptive Adversarial Training

Adversarial training (AT) is proved to reliably improve network's robust...

0 Chaojian Yu, et al. ∙

research

∙ 09/29/2022

Towards Lightweight Black-Box Attacks against Deep Neural Networks

Black-box attacks can generate adversarial examples without accessing th...

0 Chenghao Sun, et al. ∙

research

∙ 08/30/2022

Symmetric Pruning in Quantum Neural Networks

Many fundamental properties of a quantum system are captured by its Hami...

0 Xinbiao Wang, et al. ∙

research

∙ 07/25/2022

Improving Adversarial Robustness via Mutual Information Estimation

Deep neural networks (DNNs) are found to be vulnerable to adversarial no...

0 Dawei Zhou, et al. ∙

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...

0 Zhuo Huang, et al. ∙

research

∙ 06/17/2022

Understanding Robust Overfitting of Adversarial Training and Beyond

Robust overfitting widely exists in adversarial training of deep network...

0 Chaojian Yu, et al. ∙

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...

0 Lianyang Ma, et al. ∙

research

∙ 06/11/2022

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

Through using only a well-trained classifier, model-inversion (MI) attac...

0 Xiong Peng, et al. ∙

research

∙ 06/07/2022

Recent Advances for Quantum Neural Networks in Generative Learning

Quantum computers are next-generation devices that hold promise to perfo...

0 Jinkai Tian, et al. ∙

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 ...

0 De Cheng, et al. ∙

research

∙ 06/04/2022

MSR: Making Self-supervised learning Robust to Aggressive Augmentations

Most recent self-supervised learning methods learn visual representation...

0 Yingbin Bai, et al. ∙

research

∙ 05/30/2022

Robust Weight Perturbation for Adversarial Training

Overfitting widely exists in adversarial robust training of deep network...

0 Chaojian Yu, et al. ∙

research

∙ 05/27/2022

Counterfactual Fairness with Partially Known Causal Graph

Fair machine learning aims to avoid treating individuals or sub-populati...

0 Aoqi Zuo, et al. ∙

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...

0 Erdun Gao, et al. ∙

research

∙ 05/18/2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts

Pluralistic image completion focuses on generating both visually realist...

4 Xiaobo Xia, et al. ∙

research

∙ 04/15/2022

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Graph neural networks have emerged as a leading architecture for many gr...

0 Chuang Liu, et al. ∙

Tongliang Liu

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