Search Results for author: Chengchao Shen

Found 16 papers, 13 papers with code

Inter-Instance Similarity Modeling for Contrastive Learning

1 code implementation21 Jun 2023 Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang

In this paper, we propose a novel image mix method, PatchMix, for contrastive learning in Vision Transformer (ViT), to model inter-instance similarities among images.

Contrastive Learning Instance Segmentation +4

Asymmetric Patch Sampling for Contrastive Learning

1 code implementation5 Jun 2023 Chengchao Shen, Jianzhong Chen, Shu Wang, Hulin Kuang, Jin Liu, Jianxin Wang

Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning.

Contrastive Learning Instance Segmentation +3

Modeling Global Distribution for Federated Learning with Label Distribution Skew

1 code implementation17 Dec 2022 Tao Sheng, Chengchao Shen, YuAn Liu, Yeyu Ou, Zhe Qu, Jianxin Wang

It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage.

Federated Learning Generative Adversarial Network

Learning Dynamic Preference Structure Embedding From Temporal Networks

1 code implementation23 Nov 2021 Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu

Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.

Graph Sampling

Contrastive Model Inversion for Data-Free Knowledge Distillation

3 code implementations18 May 2021 Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, Mingli Song

In this paper, we propose Contrastive Model Inversion~(CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue.

Contrastive Learning Data-free Knowledge Distillation

Training Generative Adversarial Networks in One Stage

1 code implementation CVPR 2021 Chengchao Shen, Youtan Yin, Xinchao Wang, Xubin Li, Jie Song, Mingli Song

Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort.

Data-free Knowledge Distillation Image Generation

Progressive Network Grafting for Few-Shot Knowledge Distillation

2 code implementations9 Dec 2020 Chengchao Shen, Xinchao Wang, Youtan Yin, Jie Song, Sihui Luo, Mingli Song

In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category.

Knowledge Distillation Model Compression +1

DEPARA: Deep Attribution Graph for Deep Knowledge Transferability

1 code implementation CVPR 2020 Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, Mingli Song

In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.

Model Selection Transfer Learning

Data-Free Adversarial Distillation

3 code implementations23 Dec 2019 Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, Mingli Song

Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer.

Knowledge Distillation Model Compression +2

Deep Model Transferability from Attribution Maps

2 code implementations NeurIPS 2019 Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song

Exploring the transferability between heterogeneous tasks sheds light on their intrinsic interconnections, and consequently enables knowledge transfer from one task to another so as to reduce the training effort of the latter.

Transfer Learning

Customizing Student Networks From Heterogeneous Teachers via Adaptive Knowledge Amalgamation

2 code implementations ICCV 2019 Chengchao Shen, Mengqi Xue, Xinchao Wang, Jie Song, Li Sun, Mingli Song

To this end, we introduce a dual-step strategy that first extracts the task-specific knowledge from the heterogeneous teachers sharing the same sub-task, and then amalgamates the extracted knowledge to build the student network.

Amalgamating Knowledge towards Comprehensive Classification

1 code implementation7 Nov 2018 Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, Mingli Song

We propose in this paper to study a new model-reusing task, which we term as \emph{knowledge amalgamation}.

Classification General Classification

Selective Zero-Shot Classification with Augmented Attributes

no code implementations ECCV 2018 Jie Song, Chengchao Shen, Jie Lei, An-Xiang Zeng, Kairi Ou, DaCheng Tao, Mingli Song

We propose a selective zero-shot classifier based on both the human defined and the automatically discovered residual attributes.

Attribute Classification +2

Transductive Unbiased Embedding for Zero-Shot Learning

no code implementations CVPR 2018 Jie Song, Chengchao Shen, Yezhou Yang, Yang Liu, Mingli Song

Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classes.

Transductive Learning Zero-Shot Learning

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