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Masked Autoencoder~(MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training.
Adaptive mixtures of local experts
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Imagenet: A large-scale hierarchical image database
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Unsupervised visual representation learning by context prediction
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What makes imagenet good for transfer learning?
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Context encoders: Feature learning by inpainting
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Colorful image colorization
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Mask r-cnn
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Decoupled weight decay regularization
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Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
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Cascade R-CNN: delving into high quality object detection
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Self-labelling via simultaneous clustering and representation learning
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Multi-task deep neural networks for natural language understanding
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Image quality assessment through fsim, ssim, mse and psnr—a comparative study
Umme Sara, Morium Akter, and Mohammad Shorif Uddin · 2019
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Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2019
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity, 2021
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Network clustering for multi-task learning
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Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving
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Beyond distillation: Task-level mixture-of-experts for efficient inference
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Base layers: Simplifying training of large, sparse models
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Unsupervised learning of visual features by contrasting cluster assignments
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A simple framework for contrastive learning of visual representations
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, and Luke Zettlemoyer · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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A comprehensive ehr timeseries pre-training benchmark
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Scaling vision with sparse mixture of experts
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M6-t: Exploring sparse expert models and beyond
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Logme: Practical assessment of pre-trained models for transfer learning
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ibot: Image bert pre-training with online tokenizer
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Masked autoencoders enable efficient knowledge distillers
Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, and Cihang Xie · 2022
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Task-customized self-supervised pre-training with scalable dynamic routing
Zhili Liu, Jianhua Han, Kai Chen, Lanqing Hong, Hang Xu, Chunjing Xu, and Zhenguo Li · 2022
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Lemeng Wu, Mengchen Liu, Yinpeng Chen, Dongdong Chen, Xiyang Dai, and Lu Yuan · 2022
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Mixed autoencoder for self-supervised visual representation learning
Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, and Dit-Yan Yeung · 2023
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