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Embeddings produced by pre-trained deep neural networks (DNNs) are widely used; however, their efficacy for downstream tasks can vary widely.
Dominance statistics: Ordinal analyses to answer ordinal questions
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Learning multiple layers of features from tiny images
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Fine-grained visual classification of aircraft
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Cifar-100 (canadian institute for advanced research)
Alex Krizhevsky and Geoffrey Hinton · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
David Tellez, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen Van Der Laak · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Vqd: Visual query detection in natural scenes
Manoj Acharya, Karan Jariwala, and Christopher Kanan · 2019
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Pytorch image models
Ross Wightman · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Anatomy of catastrophic forgetting: Hidden representations and task semantics
Vinay Venkatesh Ramasesh, Ethan Dyer, and Maithra Raghu · 2020
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Are open set classification methods effective on large-scale datasets?
Ryne Roady, Tyler L Hayes, Ronald Kemker, Ayesha Gonzales, and Christopher Kanan · 2020
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Ssmba: Self-supervised manifold based data augmentation for improving out-of-domain robustness, 2020
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi · 2020
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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Parametric instance classification for unsupervised visual feature learning
Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, and Han Hu · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan 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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Ood-probe: A neural interpretation of out-of-domain generalization
Zining Zhu, Soroosh Shahtalebi, and Frank Rudzicz · 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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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Better plain vit baselines for imagenet-1k
Lucas Beyer, Xiaohua Zhai, and Alexander Kolesnikov · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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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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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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From local explanations to global understanding with explainable ai for trees
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
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What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson WH Lau, and Stephen Lin · 2021
Cited alongside, same era.
Fortuitous forgetting in connectionist networks
Hattie Zhou, Ankit Vani, Hugo Larochelle, and Aaron Courville · 2021
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Pan Zhou, Yichen Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, and Shuicheng Yan · 2022
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
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What happens during finetuning of vision transformers: An invariance based investigation
Gabriele Merlin, Vedant Nanda, Ruchit Rawal, and Mariya Toneva · 2023
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The tunnel effect: Building data representations in deep neural networks
Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani, Razvan Pascanu, Piotr Miłoś, and Tomasz Trzcinski · 2023
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Feature learning in deep classifiers through intermediate neural collapse
Akshay Rangamani, Marius Lindegaard, Tomer Galanti, and Tomaso A Poggio · 2023
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective
Zeyuan Yin, Eric Xing, and Zhiqiang Shen · 2023
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Deep class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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Computationally budgeted continual learning: What does matter?
Ameya Prabhu, Hasan Abed Al Kader Hammoud, Puneet K Dokania, Philip HS Torr, Ser-Nam Lim, Bernard Ghanem, and Adel Bibi · 2023
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Does progress on imagenet transfer to real-world datasets?
Alex Fang, Simon Kornblith, and Ludwig Schmidt · 2023
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Id and ood performance are sometimes inversely correlated on real-world datasets
Damien Teney, Yong Lin, Seong Joon Oh, and Ehsan Abbasnejad · 2023
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On the connection between pre-training data diversity and fine-tuning robustness
Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ali Farhadi, and Ludwig Schmidt · 2023
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Simulated annealing in early layers leads to better generalization
Amir M Sarfi, Zahra Karimpour, Muawiz Chaudhary, Nasir M Khalid, Mirco Ravanelli, Sudhir Mudur, and Eugene Belilovsky · 2023
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Self-supervised learning from images with a joint-embedding predictive architecture
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas · 2023
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In or out? fixing imagenet out-of-distribution detection evaluation
Julian Bitterwolf, Maximilian Müller, and Matthias Hein · 2023
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Convnext v2: Co-designing and scaling convnets with masked autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie · 2023
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Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks
Bálint Mucsányi, Michael Kirchhof, and Seong Joon Oh · 2024
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Convnet vs transformer, supervised vs clip: Beyond imagenet accuracy, 2024
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Dive into the chasm: Probing the gap between in-and cross-topic generalization
Andreas Waldis, Yufang Hou, and Iryna Gurevych · 2024
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Grasp: A rehearsal policy for efficient online continual learning
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Overcoming the stability gap in continual learning
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Continual learning: Applications and the road forward
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