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We propose a new technique, Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine transform (allowing comparison between different layers and networks) and fast to compute (allowing more comparisons to be calculated than with previous methods). We deploy this tool to measure the intrinsic dimensionality of layers, showing in some cases needless over-parameterization; to probe learning dynamics throughout training, finding that networks converge to final representations from the bottom up; to show where class-specific information in networks is formed; and to suggest new training regimes that simultaneously save computation and overfit less.
Author Information
Maithra Raghu (Cornell University and Google Brain)
Justin Gilmer (Google Brain)
Jason Yosinski (Uber)
Jascha Sohl-Dickstein (Google Brain)
More from the Same Authors
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2020 Poster: Finite Versus Infinite Neural Networks: an Empirical Study »
Jaehoon Lee · Samuel Schoenholz · Jeffrey Pennington · Ben Adlam · Lechao Xiao · Roman Novak · Jascha Sohl-Dickstein -
2020 Spotlight: Finite Versus Infinite Neural Networks: an Empirical Study »
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2019 Poster: Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent »
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2019 Poster: A Fourier Perspective on Model Robustness in Computer Vision »
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2019 Poster: Invertible Convolutional Flow »
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2019 Spotlight: Invertible Convolutional Flow »
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2018 Poster: Insights on representational similarity in neural networks with canonical correlation »
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2018 Poster: Sanity Checks for Saliency Maps »
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2018 Poster: PCA of high dimensional random walks with comparison to neural network training »
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2018 Spotlight: Sanity Checks for Saliency Maps »
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2018 Poster: Adversarial Examples that Fool both Computer Vision and Time-Limited Humans »
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2017 Workshop: Deep Learning: Bridging Theory and Practice »
Sanjeev Arora · Maithra Raghu · Russ Salakhutdinov · Ludwig Schmidt · Oriol Vinyals -
2017 Poster: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models »
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2017 Oral: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models »
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2016 Workshop: Brains and Bits: Neuroscience meets Machine Learning »
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2016 Poster: Exponential expressivity in deep neural networks through transient chaos »
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2015 Workshop: Statistical Methods for Understanding Neural Systems »
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2012 Poster: Training sparse natural image models with a fast Gibbs sampler of an extended state space »
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