Search Results for author: An Xiao

Found 7 papers, 2 papers with code

Greedy Network Enlarging

no code implementations31 Jul 2021 Chuanjian Liu, Kai Han, An Xiao, Yiping Deng, Wei zhang, Chunjing Xu, Yunhe Wang

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i. e., models with more MACs typically achieve better accuracy, such as EfficientNet and RegNet.

Augmented Shortcuts for Vision Transformers

3 code implementations NeurIPS 2021 Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang

Transformer models have achieved great progress on computer vision tasks recently.

Transformer in Transformer

10 code implementations NeurIPS 2021 Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang

In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT).

Fine-Grained Image Classification

GhostSR: Learning Ghost Features for Efficient Image Super-Resolution

no code implementations21 Jan 2021 Ying Nie, Kai Han, Zhenhua Liu, An Xiao, Yiping Deng, Chunjing Xu, Yunhe Wang

Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i. e., Ghost features).

Image Super-Resolution

A Survey on Vision Transformer

no code implementations23 Dec 2020 Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, DaCheng Tao

Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.

Image Classification

Circumventing Outliers of AutoAugment with Knowledge Distillation

no code implementations ECCV 2020 Longhui Wei, An Xiao, Lingxi Xie, Xin Chen, Xiaopeng Zhang, Qi Tian

AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.

Data Augmentation General Classification +1

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