Search Results for author: JieZhang Cao

Found 16 papers, 8 papers with code

SwinIR: Image Restoration Using Swin Transformer

2 code implementations23 Aug 2021 Jingyun Liang, JieZhang Cao, Guolei Sun, Kai Zhang, Luc van Gool, Radu Timofte

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Color Image Denoising Image Denoising +4

Video Super-Resolution Transformer

1 code implementation12 Jun 2021 JieZhang Cao, Yawei Li, Kai Zhang, Luc van Gool

Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.

Optical Flow Estimation Video Super-Resolution

LocalViT: Bringing Locality to Vision Transformers

2 code implementations12 Apr 2021 Yawei Li, Kai Zhang, JieZhang Cao, Radu Timofte, Luc van Gool

The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.

Image Classification

Internal Wasserstein Distance for Adversarial Attack and Defense

no code implementations13 Mar 2021 Jincheng Li, JieZhang Cao, Shuhai Zhang, Yanwu Xu, Jian Chen, Mingkui Tan

Existing attack methods on the construction of adversarial examples use such $\ell_p$ distance as a similarity metric to perturb samples.

Adversarial Attack

Learning Defense Transformers for Counterattacking Adversarial Examples

no code implementations13 Mar 2021 Jincheng Li, JieZhang Cao, Yifan Zhang, Jian Chen, Mingkui Tan

Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs.

Adversarial Defense

Improving Generative Adversarial Networks with Local Coordinate Coding

1 code implementation28 Jul 2020 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.

Generative Low-bitwidth Data Free Quantization

2 code implementations ECCV 2020 Shoukai Xu, Haokun Li, Bohan Zhuang, Jing Liu, JieZhang Cao, Chuangrun Liang, Mingkui Tan

More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method.

Fine-tuning Quantization

Online Adaptive Asymmetric Active Learning with Limited Budgets

1 code implementation18 Nov 2019 Yifan Zhang, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, JieZhang Cao, Junzhou Huang, Mingkui Tan

In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.

Active Learning Anomaly Detection

Multi-marginal Wasserstein GAN

3 code implementations NeurIPS 2019 Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation.

Image Generation Translation

Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors

no code implementations25 Sep 2019 JieZhang Cao, Jincheng Li, Xiping Hu, Peilin Zhao, Mingkui Tan

ii) the $W$-distance of a specific layer to the target distribution tends to decrease along training iterations.

Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching

no code implementations27 Sep 2018 JieZhang Cao, Yong Guo, Langyuan Mo, Peilin Zhao, Junzhou Huang, Mingkui Tan

We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.

Unsupervised Image-To-Image Translation Video Frame Interpolation +1

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

no code implementations19 Sep 2018 Yong Guo, Qi Chen, Jian Chen, Junzhou Huang, Yanwu Xu, JieZhang Cao, Peilin Zhao, Mingkui Tan

However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.

Image Super-Resolution

Adversarial Learning with Local Coordinate Coding

no code implementations ICML 2018 Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, Mingkui Tan

Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).

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