Fetching the paper…
Reading the bibliography…
Recent advancements in deep learning have been primarily driven by the use of large models trained on increasingly vast datasets.
Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Wieland Brendel and Matthias Bethge · 1904
Earlier work this paper cites.
Classification accuracy score for conditional generative models, 2019
Suman Ravuri and Oriol Vinyals · 1905
Earlier work this paper cites.
Finding the needle in the haystack with convolutions: on the benefits of architectural bias
Stéphane d’Ascoli, Levent Sagun, Joan Bruna, and Giulio Biroli · 1906
Earlier work this paper cites.
This dataset does not exist: training models from generated images, 2019
Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, and Patrick Pérez · 1911
Earlier work this paper cites.
Exploring the origins and prevalence of texture bias in convolutional neural networks
Katherine L. Hermann and Simon Kornblith · 1911
Earlier work this paper cites.
Towards understanding the spectral bias of deep learning
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, and Quanquan Gu · 1912
Earlier work this paper cites.
Analyzing and improving the image quality of stylegan, 2019
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 1912
Earlier work this paper cites.
Quantifying inductive bias: Ai learning algorithms and valiant’s learning framework
David Haussler · 1988
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations, 2020
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2010
Earlier work this paper cites.
Shape-texture debiased neural network training
Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan L. Yuille, and Cihang Xie · 2010
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Inductive bias of deep convolutional networks through pooling geometry
Nadav Cohen and Amnon Shashua · 2016
Cited alongside, same era.
Image style transfer using convolutional neural networks
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge · 2016
Cited alongside, same era.
Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
Cited alongside, same era.
Towards fairer datasets
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
Later among the works it cites.
Dissecting the high-frequency bias in convolutional neural networks
Antonio A. Abello, Roberto Hirata, and Zhangyang Wang · 2021
Later among the works it cites.
Learning to see by looking at noise, 2021
Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, and Antonio Torralba · 2021
Later among the works it cites.
A random CNN sees objects: One inductive bias of CNN and its applications
Yun-Hao Cao and Jianxin Wu · 2021
Later among the works it cites.
Generative models as a data source for multiview representation learning, 2021
Ali Jahanian, Xavier Puig, Yonglong Tian, and Phillip Isola · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diederik P. Kingma and Jimmy Ba · 2017
Cited alongside, same era.
Deep convolutional networks do not classify based on global object shape
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip Kellman · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Learning inductive biases with simple neural networks
Reuben Feinman and Brenden M. Lake · 2018
Cited alongside, same era.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2018
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium, 2018
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2018
Cited alongside, same era.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Cited alongside, same era.
Pre-training without natural images, 2021
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, and Yutaka Satoh · 2021
Later among the works it cites.
Daiqing Li, Junlin Yang, Karsten Kreis, Antonio Torralba, and Sanja Fidler · 2021
Later among the works it cites.
Repurposing gans for one-shot semantic part segmentation, 2021
Nontawat Tritrong, Pitchaporn Rewatbowornwong, and Supasorn Suwajanakorn · 2021
Later among the works it cites.
Datasetgan: Efficient labeled data factory with minimal human effort, 2021
Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, and Sanja Fidler · 2021
Later among the works it cites.
Procedural image programs for representation learning, 2022
Manel Baradad, Chun-Fu Chen, Jonas Wulff, Tongzhou Wang, Rogerio Feris, Antonio Torralba, and Phillip Isola · 2022
Later among the works it cites.
A theoretical study of inductive biases in contrastive learning, 2022
Jeff Z. HaoChen and Tengyu Ma · 2022
Later among the works it cites.
Scaling vision transformers to 22 billion parameters, 2023
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, and Neil Houlsby · 2023
Closest in time.