Fetching the paper…
Reading the bibliography…
The pretrain-finetune paradigm is a classical pipeline in visual learning.
An introduction to inequalities
Edwin F Beckenbach, Richard Bellman, and Richard Ernest Bellman · 1961
Earlier work this paper cites.
Linear discriminant analysis-a brief tutorial
Suresh Balakrishnama and Aravind Ganapathiraju · 1998
Earlier work this paper cites.
WordNet: An electronic lexical database
George A Miller · 1998
Earlier work this paper cites.
Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2007
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Various proofs of the cauchy-schwarz inequality
Hui-Hua Wu and Shanhe Wu · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, and Andrew Y Ng · 2011
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Using deep learning for image-based plant disease detection
Sharada P Mohanty, David P Hughes, and Marcel Salathé · 2016
Earlier work this paper cites.
Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Earlier work this paper cites.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Earlier work this paper cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers · 2017
Earlier work this paper cites.
Scaling sgd batch size to 32k for imagenet training
Yang You, Igor Gitman, and Boris Ginsburg · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
Cited alongside, same era.
A critical analysis of self-supervision, or what we can learn from a single image
YM Asano, C Rupprecht, and A Vedaldi · 2019
Cited alongside, same era.
Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al · 2019
Cited alongside, same era.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Cited alongside, same era.
Negative margin matters: Understanding margin in few-shot classification
Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu · 2020
Later among the works it cites.
Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
Later among the works it cites.
Kaokore: A pre-modern japanese art facial expression dataset
Yingtao Tian, Chikahiko Suzuki, Tarin Clanuwat, Mikel Bober-Irizar, Alex Lamb, and Asanobu Kitamoto · 2020
Later among the works it cites.
Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
Later among the works it cites.
A comparison of machine learning methods for cross-domain few-shot learning
Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael Mayo · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
Cited alongside, same era.
Towards understanding the transferability of deep representations
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2019
Cited alongside, same era.
Deepweeds: A multiclass weed species image dataset for deep learning
Alex Olsen, Dmitry A Konovalov, Bronson Philippa, Peter Ridd, Jake C Wood, Jamie Johns, Wesley Banks, Benjamin Girgenti, Owen Kenny, James Whinney, et al · 2019
Cited alongside, same era.
Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Detectron2
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2019
Cited alongside, same era.
Longhui Wei, Lingxi Xie, Jianzhong He, Jianlong Chang, Xiaopeng Zhang, Wengang Zhou, Houqiang Li, and Qi Tian · 2020
Later among the works it cites.
What makes instance discrimination good for transfer learning?
Nanxuan Zhao, Zhirong Wu, Rynson WH Lau, and Stephen Lin · 2020
Later among the works it cites.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
Closest in time.
Multisiam: Self-supervised multi-instance siamese representation learning for autonomous driving
Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, and Dit-Yan Yeung · 2021
Closest in time.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Closest in time.
With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
Closest in time.
How well do self-supervised models transfer?
Linus Ericsson, Henry Gouk, and Timothy M Hospedales · 2021
Closest in time.
A broad study on the transferability of visual representations with contrastive learning
Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Richard Radke, and Rogerio Feris · 2021
Closest in time.
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
Closest in time.
Concept generalization in visual representation learning
Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus, and Karteek Alahari · 2021
Closest in time.
Mutual crf-gnn for few-shot learning
Shixiang Tang, Dapeng Chen, Lei Bai, Kaijian Liu, Yixiao Ge, and Wanli Ouyang · 2021
Closest in time.
Gradient regularized contrastive learning for continual domain adaptation
Shixiang Tang, Peng Su, Dapeng Chen, and Wanli Ouyang · 2021
Closest in time.
Detco: Unsupervised contrastive learning for object detection
Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Zhenguo Li, and Ping Luo · 2021
Closest in time.
Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu · 2021
Closest in time.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Closest in time.