Semantic-Aware Auto-Encoders for Self-supervised Representation Learning

Author:

Wang Guangrun1,Tang Yansong2,Lin Liang3,Torr Philip H.S.1

Affiliation:

1. University of Oxford

2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University

3. Sun Yat-sen University

Funder

EPSRC

MURI

China National Key R&D Program

China National Natural Science Foundation

Guangdong Natural Science Foundation

Royal Academy of Engineering of the UK

Publisher

IEEE

Reference67 articles.

1. M2m-gan: Many-to-many generative adversarial transfer learning for person re-identification;liang;Arxiv preprint arXiv,2018

2. Benchmarking detection transfer learning with vision transformers;li;Arxiv preprint arXiv,2021

3. Image-to-image translation with conditional adversar-ial networks;isola;2017 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017,2017

4. Batch normalization: Accelerating deep network training by reducing internal co-variate shift;ioffe;Proceedings of the 32nd International Conference on Ma-chine Learning ICML 2015,2015

5. Deep networks with stochastic depth;huang;Computer Vision-ECCV 2016-14th European Con-ference,2016

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1. autoSMIM: Automatic Superpixel-Based Masked Image Modeling for Skin Lesion Segmentation;IEEE Transactions on Medical Imaging;2023-12

2. Attention-Guided Contrastive Masked Image Modeling for Transformer-Based Self-Supervised Learning;2023 IEEE International Conference on Image Processing (ICIP);2023-10-08

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