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Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.
An acceleration framework for high resolution image synthesis
Jinlin Liu, Yuan Yao, and Jianqiang Ren · 1909
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Gradient-Based Learning Applied to Document Recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Honglak Lee, and Andrew Y Ng · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Predicting Parameters in Deep Learning
Misha Denil, Babak Shakibi, Laurent Dinh, and Marc’Aurelio Ranzato · 2013
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Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2013
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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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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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Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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Learning feed-forward one-shot learners
Luca Bertinetto, João F Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi · 2016
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Net2Net: Accelerating Learning via Knowledge Transfer
Tianqi Chen, Ian Goodfellow, and Jonathon Shlens · 2016
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David Ha, Andrew Dai, and Quoc V. Le · 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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All you need is a good init
Dmytro Mishkin and Jiri Matas · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Generating Neural Networks with Neural Networks
Lior Deutsch · 2018
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UMAP: Uniform Manifold Approximation and Projection
Leland McInnes, John Healy, and Nathaniel Saul · 2018
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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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Predicting Neural Network Accuracy from Weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya Tolstikhin · 2020
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A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Instance-conditioned gan
Arantxa Casanova, Marlène Careil, Jakob Verbeek, Michal Drozdzal, and Adriana Romero Soriano · 2021
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Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Adversarial distillation of bayesian neural network posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, and Richard Zemel · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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MetaInit: Initializing learning by learning to initialize
Yann N Dauphin and Samuel Schoenholz · 2019
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A baseline for few-shot image classification
Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2019
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Auto-embedding generative adversarial networks for high resolution image synthesis
Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, and Mingkui Tan · 2019
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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Boris Knyazev, Michal Drozdzal, Graham W. Taylor, and Adriana Romero-Soriano · 2021
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Charles H Martin, Tongsu Serena Peng, and Michael W Mahoney · 2021
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Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types
Thomas Mensink, Jasper Uijlings, Alina Kuznetsova, Michael Gygli, and Vittorio Ferrari · 2021
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Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Konstantin Schürholt, Dimche Kostadinov, and Damian Borth · 2021
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Zoo-Tuning: Adaptive Transfer from a Zoo of Models
Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, and Mingsheng Long · 2021
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Recurrent Parameter Generators, July 2021
Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, and Yann LeCun · 2021
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Gradinit: Learning to initialize neural networks for stable and efficient training
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W Ronny Huang, and Tom Goldstein · 2021
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Learning to Learn with Generative Models of Neural Network Checkpoints, September 2022
William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A. Efros, and Jitendra Malik · 2022
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Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt, Diyar Taskiran, Boris Knyazev, Xavier Giró-i-Nieto, and Damian Borth · 2022
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HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
Andrey Zhmoginov, Mark Sandler, and Max Vladymyrov · 2022
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