Search Results for author: Yonggang Zhang

Found 16 papers, 11 papers with code

Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

no code implementations ICML 2020 Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian

To obtain sufficient knowledge for crafting adversarial examples, previous methods query the target model with inputs that are perturbed with different searching directions.

Continual Named Entity Recognition without Catastrophic Forgetting

1 code implementation23 Oct 2023 Duzhen Zhang, Wei Cong, Jiahua Dong, Yahan Yu, Xiuyi Chen, Yonggang Zhang, Zhen Fang

This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type.

Continual Named Entity Recognition named-entity-recognition +1

Moderately Distributional Exploration for Domain Generalization

1 code implementation27 Apr 2023 Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian

We show that MODE can endow models with provable generalization performance on unknown target domains.

Domain Generalization

Hard Sample Matters a Lot in Zero-Shot Quantization

1 code implementation CVPR 2023 Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan

Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models.


FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

1 code implementation3 Mar 2023 Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran, Sunwoo Lee, Shaohuai Shi, Yonggang Zhang, Yuxin Wang, Alex Qiaozhong Liang, Salman Avestimehr, Chaoyang He

It improves the training efficiency, remarkably relaxes the requirements on the hardware, and supports efficient large-scale FL experiments with stateful clients by: (1) sequential training clients on devices; (2) decomposing original aggregation into local and global aggregation on devices and server respectively; (3) scheduling tasks to mitigate straggler problems and enhance computing utility; (4) distributed client state manager to support various FL algorithms.

Federated Learning Scheduling

Watermarking for Out-of-distribution Detection

1 code implementation27 Oct 2022 Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han

Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models.

Out-of-Distribution Detection

Towards Lightweight Black-Box Attacks against Deep Neural Networks

1 code implementation29 Sep 2022 Chenghao Sun, Yonggang Zhang, Wan Chaoqun, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian

As it is hard to mitigate the approximation error with few available samples, we propose Error TransFormer (ETF) for lightweight attacks.

Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

1 code implementation6 Jun 2022 Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu

In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift.

Federated Learning

Prompt Distribution Learning

no code implementations CVPR 2022 Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.

Language Modelling

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

1 code implementation ICLR 2022 Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i. e., Graph Modification Attack (GMA).

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

3 code implementations11 Feb 2022 Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

Despite recent success in using the invariance principle for out-of-distribution (OOD) generalization on Euclidean data (e. g., images), studies on graph data are still limited.

Drug Discovery Graph Learning +1

Meta Convolutional Neural Networks for Single Domain Generalization

no code implementations CVPR 2022 Chaoqun Wan, Xu Shen, Yonggang Zhang, Zhiheng Yin, Xinmei Tian, Feng Gao, Jianqiang Huang, Xian-Sheng Hua

Taking meta features as reference, we propose compositional operations to eliminate irrelevant features of local convolutional features by an addressing process and then to reformulate the convolutional feature maps as a composition of related meta features.

Domain Generalization

Class-Disentanglement and Applications in Adversarial Detection and Defense

no code implementations NeurIPS 2021 Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, DaCheng Tao

In this paper, we propose ''class-disentanglement'' that trains a variational autoencoder $G(\cdot)$ to extract this class-dependent information as $x - G(x)$ via a trade-off between reconstructing $x$ by $G(x)$ and classifying $x$ by $D(x-G(x))$, where the former competes with the latter in decomposing $x$ so the latter retains only necessary information for classification in $x-G(x)$.

Adversarial Defense Disentanglement

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