Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.
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Updated
Mar 27, 2024 - MATLAB
Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.
[ICLR 2022] Training L_inf-dist-net with faster acceleration and better training strategies
Build and train Lipschitz-constrained networks: PyTorch implementation of 1-Lipschitz layers. For TensorFlow/Keras implementation, see https://github.com/deel-ai/deel-lip
Code for Spectral Norm of Convolutional Layers with Circular and Zero Paddings and Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
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