b'Bo Han'

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

∙ 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...

0 Jingfeng Zhang, et al. ∙

research

∙ 05/25/2023

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

Adversarial detection aims to determine whether a given sample is an adv...

0 Shuhai Zhang, et al. ∙

research

∙ 05/16/2023

AMD: Autoregressive Motion Diffusion

Human motion generation aims to produce plausible human motion sequences...

0 Bo Han, et al. ∙

research

∙ 05/14/2023

SongDriver2: Real-time Emotion-based Music Arrangement with Soft Transition

Real-time emotion-based music arrangement, which aims to transform a giv...

0 Zihao Wang, et al. ∙

research

∙ 04/30/2023

Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks

Adversarial training (AT) is a robust learning algorithm that can defend...

0 Jingfeng Zhang, et al. ∙

research

∙ 04/27/2023

Moderately Distributional Exploration for Domain Generalization

Domain generalization (DG) aims to tackle the distribution shift between...

6 Rui Dai, et al. ∙

research

∙ 04/22/2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization

A common explanation for the failure of out-of-distribution (OOD) genera...

0 Yongqiang Chen, et al. ∙

research

∙ 04/06/2023

SketchFFusion: Sketch-guided image editing with diffusion model

Sketch-guided image editing aims to achieve local fine-tuning of the ima...

0 Weihang Mao, et al. ∙

research

∙ 03/24/2023

Hard Sample Matters a Lot in Zero-Shot Quantization

Zero-shot quantization (ZSQ) is promising for compressing and accelerati...

0 Huantong Li, 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

∙ 03/09/2023

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

∙ 03/04/2023

Federated Semi-Supervised Learning with Annotation Heterogeneity

Federated Semi-Supervised Learning (FSSL) aims to learn a global model f...

0 Xinyi Shang, 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

∙ 02/19/2023

Latent Class-Conditional Noise Model

Learning with noisy labels has become imperative in the Big Data era, wh...

0 Jiangchao Yao, et al. ∙

research

∙ 02/17/2023

Towards Zero-trust Security for the Metaverse

By focusing on immersive interaction among users, the burgeoning Metaver...

0 Ruizhi Cheng, et al. ∙

research

∙ 01/31/2023

Zero3D: Semantic-Driven Multi-Category 3D Shape Generation

Semantic-driven 3D shape generation aims to generate 3D objects conditio...

0 Bo Han, et al. ∙

research

∙ 01/22/2023

Dance2MIDI: Dance-driven multi-instruments music generation

Dance-driven music generation aims to generate musical pieces conditione...

0 Bo Han, et al. ∙

research

∙ 11/23/2022

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the sea...

0 Xin He, 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 ...

0 Jianan Zhou, et al. ∙

research

∙ 10/26/2022

Is Out-of-Distribution Detection Learnable?

Supervised learning aims to train a classifier under the assumption that...

0 Zhen Fang, 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

∙ 08/21/2022

Combating Noisy-Labeled and Imbalanced Data by Two Stage Bi-Dimensional Sample Selection

Robust learning on noisy-labeled data has been an important task in real...

0 Yiliang Zhang, 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

∙ 07/15/2022

FRAS: Federated Reinforcement Learning empowered Adaptive Point Cloud Video Streaming

Point cloud video transmission is challenging due to high encoding/decod...

0 Yu Gao, 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

∙ 07/07/2022

Device-Cloud Collaborative Recommendation via Meta Controller

On-device machine learning enables the lightweight deployment of recomme...

0 Jiangchao Yao, et al. ∙

research

∙ 06/27/2022

Efficient Private SCO for Heavy-Tailed Data via Clipping

We consider stochastic convex optimization for heavy-tailed data with th...

0 Chenhan Jin, 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

∙ 06/15/2022

Pareto Invariant Risk Minimization

Despite the success of invariant risk minimization (IRM) in tackling the...

5 Yongqiang Chen, et al. ∙

research

∙ 06/15/2022

Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

The AutoAttack (AA) has been the most reliable method to evaluate advers...

0 Ruize Gao, 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

∙ 06/06/2022

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

In federated learning (FL), model performance typically suffers from cli...

0 Zhenheng Tang, 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

Learning Adaptive Propagation for Knowledge Graph Reasoning

Due to the success of Graph Neural Networks (GNNs) in learning from grap...

0 Yongqi Zhang, 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

∙ 05/25/2022

Contrastive Learning with Boosted Memorization

Self-supervised learning has achieved a great success in the representat...

0 Zhihan Zhou, et al. ∙

research

∙ 05/20/2022

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

Federated learning (FL) aims at training a global model on the server si...

0 Zhuowei Wang, 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

∙ 05/06/2022

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization

Nonconvex regularization has been popularly used in low-rank matrix lear...

2 Quanming Yao, et al. ∙

research

∙ 02/16/2022

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

Recently Graph Injection Attack (GIA) emerges as a practical attack scen...

3 Yongqiang Chen, et al. ∙

research

∙ 02/11/2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs

Despite recent developments in using the invariance principle from causa...

10 Yongqiang Chen, et al. ∙

research

∙ 01/30/2022

Will Metaverse be NextG Internet? Vision, Hype, and Reality

Metaverse, with the combination of the prefix "meta" (meaning transcendi...

0 Ruizhi Cheng, et al. ∙

research

∙ 01/30/2022

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Algorithms which minimize the averaged loss have been widely designed fo...

0 Yexiong Lin, et al. ∙

research

∙ 01/16/2022

Nebula: Reliable Low-latency Video Transmission for Mobile Cloud Gaming

Mobile cloud gaming enables high-end games on constrained devices by str...

0 Ahmad Alhilal, et al. ∙

research

∙ 01/15/2022

Realtime 3D Object Detection for Headsets

Mobile headsets should be capable of understanding 3D physical environme...

5 Yongjie Guan, et al. ∙

Bo Han

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