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Arxiv
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Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
- Modern neural networks are usually highly over-parameterized.
- This work studies the rank of convolutional neural networks (CNNs) trained by gradient descent.
- CNNs trained with gradient descent are found to be robust to image background noises.
- Theoretical case study and experiments on synthetic and real datasets support the claim.
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