Alternatives and similar repositories for admm_nn

Users that are interested in admm_nn are comparing it to the libraries listed below

• huangleiBuaa / ONI
  PyTorch and Torch implementation for our accepted CVPR 2020 paper (Oral): Controllable Orthogonalization in Training DNNs
  ☆25Updated 4 years ago
• huangleiBuaa / OthogonalWN
  This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonali…
  ☆57Updated 5 years ago
• VITA-Group / Orthogonality-in-CNNs
  [NeurIPS '18] "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?" Official Implementation.
  ☆129Updated 3 years ago
• janericlenssen / group_equivariant_capsules_pytorch
  Pytorch implementation of Group Equivariant Capsule Networks
  ☆29Updated 6 years ago
• ricvolpi / domain-shift-robustness
  Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019
  ☆27Updated 5 years ago
• musikisomorphie / swd
  unsupervised video and image generation
  ☆58Updated 5 years ago
• jh-jeong / selective-convolution
  Code for the paper "Training CNNs with Selective Allocation of Channels" (ICML 2019)
  ☆25Updated 6 years ago
• icml-mcbn / mcbn
  ☆53Updated 7 years ago
• fjxmlzn / PacGAN
  [NeurIPS 2018] [JSAIT] PacGAN: The power of two samples in generative adversarial networks
  ☆81Updated 5 years ago
• KaosEngineer / PriorNetworks-OLD
  ☆42Updated 6 years ago
• yangsenius / learning-to-learn-by-pytorch
  "Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.
  ☆61Updated 5 years ago
• PotatoThanh / ADMM-NeuralNetworks
  Training Neural Networks Without Gradients: A ADMM Approach
  ☆43Updated 7 years ago
• MinhyungCho / riemannian-batch-normalization-pytorch
  Riemannian approach to batch normalization
  ☆22Updated 7 years ago
• jarrelscy / iResnet
  Non official pytorch implementation of i-Resnet, invertible residual networks.
  ☆25Updated 3 years ago
• raj-shah / BayesianCNN
  Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Gal et al. 2015
  ☆36Updated 7 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆66Updated 2 years ago
• yoonholee / MT-net
  Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"
  ☆38Updated 6 years ago
• jovitalukasik / vs_gae
  ☆10Updated last year
• haseebs / Pseudo-rehearsal-Incremental-Learning
  Pytorch implementation of paper "Distillation Techniques for Pseudo-rehearsal Based Incremental Learning"
  ☆14Updated 6 years ago
• EkdeepSLubana / flowandprune
  Codebase for the paper "A Gradient Flow Framework for Analyzing Network Pruning"
  ☆20Updated 4 years ago
• rflamary / JDOT
  Joint distribution optimal transportation for domain adaptation
  ☆102Updated 7 years ago
• YBZh / CDAN-re-implement
  The re-implement of NIPS 2018 paper: Conditional Adversarial Domain Adaptation
  ☆22Updated 7 years ago
• eifuentes / swae-pytorch
  Implementation of the Sliced Wasserstein Autoencoder using PyTorch
  ☆102Updated 7 years ago
• BaoWangMath / EnResNet
  ☆19Updated 6 years ago
• xu-ji / ARM
  Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
  ☆11Updated 5 years ago
• kylehkhsu / cactus-protonets
  Code for Unsupervised Learning via Meta-Learning.
  ☆67Updated 4 years ago
• ColinQiyangLi / LConvNet
  Implementation of Methods Proposed in Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks (NeurIPS 2019)
  ☆35Updated 5 years ago
• 2prime / LM-ResNet
  Code For Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical Differential Equations
  ☆16Updated 6 years ago
• VITA-Group / Once-for-All-Adversarial-Training
  [NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong C…
  ☆44Updated 3 years ago
• minhtannguyen / SRSGD
  Code base for SRSGD.
  ☆29Updated 5 years ago