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Understanding what information neural networks capture is an essential problem in deep learning, and studying whether different models capture similar features is an initial step to achieve this goal.
Learning multiple layers of features from tiny images
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Very deep convolutional networks for large-scale image recognition
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Convergent Learning: Do different neural networks learn the same representations?
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Revisiting self-supervised visual representation learning
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Similarity of neural network representations revisited
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Towards explaining the regularization effect of initial large learning rate in training neural networks
Yuanzhi Li, Colin Wei, and Tengyu Ma · 2019
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Image synthesis with a single (robust) classifier
Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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A survey on image data augmentation for deep learning
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Connor Shorten and Taghi M Khoshgoftaar · 2019
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Robustness may be at odds with accuracy
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A simple framework for contrastive learning of visual representations
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Knowledge consistency between neural networks and beyond
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