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The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning.
Canonical correlation analysis: An overview with application to learning methods
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A survey on transfer learning
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Imagenet large scale visual recognition challenge
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Aggregated residual transformations for deep neural networks
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Barret Zoph and Quoc V. Le · 2017
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
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Han Cai, Ligeng Zhu, and Song Han · 2019
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton · 2019
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Snip: single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip H. S. Torr · 2019
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Data-free quantization through weight equalization and bias correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Patient knowledge distillation for BERT model compression
Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu · 2019
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Pytorch image models, 2019
Ross Wightman · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Universally slimmable networks and improved training techniques
Dynamic slimmable network
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Swin transformer: Hierarchical vision transformer using shifted windows
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Neural architecture search without training
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Learning transferable visual models from natural language supervision
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Jiahui Yu and Thomas S. Huang · 2019
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Slimmable neural networks
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Hrank: Filter pruning using high-rank feature map
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Green ai
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni · 2020
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Segmenter: Transformer for semantic segmentation
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Training data-efficient image transformers & distillation through attention
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition
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Segformer: Simple and efficient design for semantic segmentation with transformers
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Focal self-attention for local-global interactions in vision transformers
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Beit: BERT pre-training of image transformers
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Masked autoencoders are scalable vision learners
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Green hierarchical vision transformer for masked image modeling
Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, and Toshihiko Yamasaki · 2022
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Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross B. Girshick, and Kaiming He · 2022
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Q-vit: Accurate and fully quantized low-bit vision transformer
Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, and Guodong Guo · 2022
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Fq-vit: Post-training quantization for fully quantized vision transformer
Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, and Shuchang Zhou · 2022
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Less is more: Pay less attention in vision transformers
Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu, and Jianfei Cai · 2022
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Deep model reassembly
Xingyi Yang, Daquan Zhou, Songhua Liu, Jingwen Ye, and Xinchao Wang · 2022
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Image BERT pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan L. Yuille, and Tao Kong · 2022
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