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Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models.
Retinal noise and absolute threshold
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Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Hierarchical mixtures of experts and the EM algorithm
Michael I Jordan and Robert A Jacobs · 1994
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Retinal processing near absolute threshold: from behavior to mechanism
Greg D Field, Alapakkam P Sampath, and Fred Rieke · 2005
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Structure of cone photoreceptors
Debarshi Mustafi, Andreas H Engel, and Krzysztof Palczewski · 2009
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Learning multiple layers of features from tiny images
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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The absolute threshold of cone vision
Darren Koenig and Heidi Hofer · 2011
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Learning factored representations in a deep mixture of experts
David Eigen, Marc’Aurelio Ranzato, and Ilya Sutskever · 2013
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Mixture of experts: a literature survey
Saeed Masoudnia and Reza Ebrahimpour · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Learning to select data for transfer learning with bayesian optimization
Sebastian Ruder and Barbara Plank · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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The primate fovea: structure, function and development
Andreas Bringmann, Steffen Syrbe, Katja Görner, Johannes Kacza, Mike Francke, Peter Wiedemann, and Andreas Reichenbach · 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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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Dasnet: Dynamic activation sparsity for neural network efficiency improvement
Qing Yang, Jiachen Mao, Zuoguan Wang, and Hai Li · 2019
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Sparse reram engine: Joint exploration of activation and weight sparsity in compressed neural networks
Tzu-Hsien Yang, Hsiang-Yun Cheng, Chia-Lin Yang, I-Ching Tseng, Han-Wen Hu, Hung-Sheng Chang, and Hsiang-Pang Li · 2019
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A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda · 2020
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Occuseg: Occupancy-aware 3d instance segmentation
Lei Han, Tian Zheng, Lan Xu, and Lu Fang · 2020
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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Fda: Fourier domain adaptation for semantic segmentation
Yanchao Yang and Stefano Soatto · 2020
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Inducing and exploiting activation sparsity for fast inference on deep neural networks
Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Nir Shavit, and Dan Alistarh · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Prime editing for precise and highly versatile genome manipulation
Peter J Chen and David R Liu · 2023
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Manipllm: Embodied multimodal large language model for object-centric robotic manipulation
Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong · 2023
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Imagemanip: Image-based robotic manipulation with affordance-guided next view selection
Xiaoqi Li, Yanzi Wang, Yan Shen, Ponomarenko Iaroslav, Haoran Lu, Qianxu Wang, Boshi An, Jiaming Liu, and Hao Dong · 2023
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Planning-oriented autonomous driving
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al · 2023
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Textdeformer: Geometry manipulation using text guidance
William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, and Rana Hanocka · 2023
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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A fourier-based framework for domain generalization
Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yanfeng Wang, and Qi Tian · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Acdc: The adverse conditions dataset with correspondences for semantic driving scene understanding
Christos Sakaridis, Dengxin Dai, and Luc Van Gool · 2021
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Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
Cited alongside, same era.
Hash layers for large sparse models
Stephen Roller, Sainbayar Sukhbaatar, Arthur Szlam, and Jason Weston · 2021
Cited alongside, same era.
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Ecotta: Memory-efficient continual test-time adaptation via self-distilled regularization
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
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A probabilistic framework for lifelong test-time adaptation
Dhanajit Brahma and Piyush Rai · 2023
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Vida: Homeostatic visual domain adapter for continual test time adaptation
Jiaming Liu, Senqiao Yang, Peidong Jia, Ming Lu, Yandong Guo, Wei Xue, and Shanghang Zhang · 2023
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Exploring sparse visual prompt for cross-domain semantic segmentation
Senqiao Yang, Jiarui Wu, Jiaming Liu, Xiaoqi Li, Qizhe Zhang, Mingjie Pan, and Shanghang Zhang · 2023
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Adaptive distribution masked autoencoders for continual test-time adaptation
Jiaming Liu, Ran Xu, Senqiao Yang, Renrui Zhang, Qizhe Zhang, Zehui Chen, Yandong Guo, and Shanghang Zhang · 2023
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Decorate the newcomers: Visual domain prompt for continual test time adaptation
Yulu Gan, Yan Bai, Yihang Lou, Xianzheng Ma, Renrui Zhang, Nian Shi, and Lin Luo · 2023
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Distribution-aware continual test time adaptation for semantic segmentation
Jiayi Ni, Senqiao Yang, Jiaming Liu, Xiaoqi Li, Wenyu Jiao, Ran Xu, Zehui Chen, Yi Liu, and Shanghang Zhang · 2023
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Sparsevit: Revisiting activation sparsity for efficient high-resolution vision transformer
Xuanyao Chen, Zhijian Liu, Haotian Tang, Li Yi, Hang Zhao, and Song Han · 2023
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Accelerating deep neural networks via semi-structured activation sparsity
Matteo Grimaldi, Darshan C. Ganji, Ivan Lazarevich, and Sudhakar Sah · 2023
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Relu strikes back: Exploiting activation sparsity in large language models, 2023
Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta, Carlo C Del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, and Mehrdad Farajtabar · 2023
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Sira: Sparse mixture of low rank adaptation
Yun Zhu, Nevan Wichers, Chu-Cheng Lin, Xinyi Wang, Tianlong Chen, Lei Shu, Han Lu, Canoee Liu, Liangchen Luo, Jindong Chen, et al · 2023
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Moec: Mixture of experts implicit neural compression
Jianchen Zhao, Cheng-Ching Tseng, Ming Lu, Ruichuan An, Xiaobao Wei, He Sun, and Shanghang Zhang · 2023
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Robust mean teacher for continual and gradual test-time adaptation
Mario Döbler, Robert A Marsden, and Bin Yang · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Efficient deweahter mixture-of-experts with uncertainty-aware feature-wise linear modulation
Rongyu Zhang, Yulin Luo, Jiaming Liu, Huanrui Yang, Zhen Dong, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Yuan Du, et al · 2024
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Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving
Xiaoyu Tian, Tao Jiang, Longfei Yun, Yucheng Mao, Huitong Yang, Yue Wang, Yilun Wang, and Hang Zhao · 2024
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Emergence of shape bias in convolutional neural networks through activation sparsity
Tianqin Li, Ziqi Wen, Yangfan Li, and Tai Sing Lee · 2024
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Multi-level personalized federated learning on heterogeneous and long-tailed data
Rongyu Zhang, Yun Chen, Chenrui Wu, Fangxin Wang, and Bo Li · 2024
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Intuition-aware mixture-of-rank-1-experts for parameter efficient finetuning
Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du, and Shanghang Zhang · 2024
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Becotta: Input-dependent online blending of experts for continual test-time adaptation
Daeun Lee, Jaehong Yoon, and Sung Ju Hwang · 2024
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Prosparse: Introducing and enhancing intrinsic activation sparsity within large language models, 2024
Chenyang Song, Xu Han, Zhengyan Zhang, Shengding Hu, Xiyu Shi, Kuai Li, Chen Chen, Zhiyuan Liu, Guangli Li, Tao Yang, and Maosong Sun · 2024
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