Search Results for author: Xinwen Hou

Found 16 papers, 6 papers with code

Meticulously Selecting 1% of the Dataset for Pre-training! Generating Differentially Private Images Data with Semantics Query

no code implementations19 Oct 2023 Kecen Li, Chen Gong, Zhixiang Li, Yuzhong Zhao, Xinwen Hou, Tianhao Wang

Then, this function assists in querying the semantic distribution of the sensitive dataset, facilitating the selection of data from the public dataset with analogous semantics for pre-training.

Image Generation

Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning

1 code implementation1 Apr 2023 Hao Chen, Chen Gong, Yizhe WANG, Xinwen Hou

This paper proposes the Recovery Triggered States (RTS) method, a novel approach that effectively protects the victim agents from backdoor attacks.

Backdoor Attack reinforcement-learning

Centralized Cooperative Exploration Policy for Continuous Control Tasks

1 code implementation6 Jan 2023 Chao Li, Chen Gong, Qiang He, Xinwen Hou, Yu Liu

To explicitly encourage exploration in continuous control tasks, we propose CCEP (Centralized Cooperative Exploration Policy), which utilizes underestimation and overestimation of value functions to maintain the capacity of exploration.

Continuous Control

Template-guided Hierarchical Feature Restoration for Anomaly Detection

no code implementations ICCV 2023 Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, Xinwen Hou

Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration.

Anomaly Detection

Unsupervised Domain Adaptation GAN Inversion for Image Editing

no code implementations22 Nov 2022 Siyu Xing, Chen Gong, Hewei Guo, Xiao-Yu Zhang, Xinwen Hou, Yu Liu

In this paper, we resolve this problem by introducing Unsupervised Domain Adaptation (UDA) into the Inversion process, namely UDA-Inversion, for both high-quality and low-quality image inversion and editing.

Image Reconstruction Unsupervised Domain Adaptation

BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets

1 code implementation7 Oct 2022 Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Kecen Li, Arunesh Sinha, Bowen Xu, Xinwen Hou, David Lo, Tianhao Wang

Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack.

Autonomous Driving Backdoor Attack +3

Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning

no code implementations CVPR 2023 Qiang He, Huangyuan Su, Jieyu Zhang, Xinwen Hou

In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property.

Continuous Control reinforcement-learning +2

Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

1 code implementation9 Dec 2021 Yunpeng Bai, Chen Gong, Bin Zhang, Guoliang Fan, Xinwen Hou, Yu Liu

HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards.

reinforcement-learning Reinforcement Learning (RL) +4

The $f$-Divergence Reinforcement Learning Framework

no code implementations24 Sep 2021 Chen Gong, Qiang He, Yunpeng Bai, Zhou Yang, Xiaoyu Chen, Xinwen Hou, Xianjie Zhang, Yu Liu, Guoliang Fan

In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.

Decision Making Mathematical Proofs +2

LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders

no code implementations22 Sep 2021 Xiaoyu Chen, Chen Gong, Qiang He, Xinwen Hou, Yu Liu

Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications.

Image Generation

MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning

no code implementations22 Sep 2021 Qiang He, Huangyuan Su, Chen Gong, Xinwen Hou

During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time.

Gaussian Processes Q-Learning +2

POPO: Pessimistic Offline Policy Optimization

1 code implementation26 Dec 2020 Qiang He, Xinwen Hou

Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment.

Offline RL Q-Learning +1

WD3: Taming the Estimation Bias in Deep Reinforcement Learning

no code implementations18 Jun 2020 Qiang He, Xinwen Hou

To obtain a more precise estimation for value function, we unify these two opposites and propose a novel algorithm \underline{W}eighted \underline{D}elayed \underline{D}eep \underline{D}eterministic Policy Gradient (WD3), which can eliminate the estimation bias and further improve the performance by weighting a pair of critics.

Continuous Control OpenAI Gym +2

Highway Transformer: Self-Gating Enhanced Self-Attentive Networks

1 code implementation ACL 2020 Yekun Chai, Shuo Jin, Xinwen Hou

Self-attention mechanisms have made striking state-of-the-art (SOTA) progress in various sequence learning tasks, standing on the multi-headed dot product attention by attending to all the global contexts at different locations.

Learning Representations in Reinforcement Learning: an Information Bottleneck Approach

no code implementations ICLR 2020 Yingjun Pei, Xinwen Hou

In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms. We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound.

reinforcement-learning Reinforcement Learning (RL) +1

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

no code implementations18 Nov 2019 Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.

reinforcement-learning Reinforcement Learning (RL)

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