juntang-zhuang / explain_invertibleLinks
repo for "Decision explanation and feature importance for invertible networks"
☆14Updated 5 years ago
Alternatives and similar repositories for explain_invertible
Users that are interested in explain_invertible are comparing it to the libraries listed below
Sorting:
- ☆53Updated 7 years ago
- Mutual Information Neural Estimator implemented in Tensorflow☆46Updated 6 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Pytorch implementation of Group Equivariant Capsule Networks☆29Updated 6 years ago
- Code for Sliced Gromov-Wasserstein☆69Updated 5 years ago
- ☆25Updated 3 years ago
- Official Pytorch code for (AAAI 2020) paper "Capsule Routing via Variational Bayes", https://arxiv.org/pdf/1905.11455.pdf☆103Updated 4 years ago
- Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE☆92Updated 4 years ago
- ☆32Updated 7 years ago
- ☆83Updated last year
- [ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).☆56Updated 3 years ago
- Uncertainty Autoencoders, AISTATS 2019☆55Updated 6 years ago
- ☆19Updated 5 years ago
- Implementation of LDMnet in pytorch☆22Updated 6 years ago
- Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding☆73Updated 3 years ago
- Robust Optimal Transport code☆43Updated 2 years ago
- Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)☆100Updated 3 years ago
- ☆46Updated 4 years ago
- Uncertainty interpretations of the neural network☆32Updated 7 years ago
- LVAE: Ladder Variational Auto-Encoders (NIPS 2016) with TensorFlow.☆16Updated 7 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆103Updated 6 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Pytorch implementation of contractive autoencoder on MNIST dataset☆53Updated 7 years ago
- Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)☆35Updated 5 years ago
- ☆17Updated 4 years ago
- Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.☆38Updated 3 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- Nonlinear SVGD for Learning Diversified Mixture Models☆13Updated 6 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago