Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.

Build and train Lipschitz-constrained networks: PyTorch implementation of 1-Lipschitz layers. For TensorFlow/Keras implementation, see https://github.com/deel-ai/deel-lip

[ICLR 2022] Training L_inf-dist-net with faster acceleration and better training strategies

Code for Spectral Norm of Convolutional Layers with Circular and Zero Paddings and Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration

D<ee>p Learning [dev library]