Alternatives and similar repositories for Sparse-Autoencoders

Users that are interested in Sparse-Autoencoders are comparing it to the libraries listed below

• samotiian / CCSA
  "Unified Deep Supervised Domain Adaptation and Generalization" (ICCV 2017)
  ☆103Updated 5 years ago
• animebing / mnist_tsne
  t-sne visualization of mnist images when feature is represented by raw pixels and cnn learned feature
  ☆64Updated 7 years ago
• Heliot7 / open-set-da
  Open set domain adaptation
  ☆86Updated 5 years ago
• vbezgachev / semi-supervised-gan
  PyTorch implementation of semi-supervised GAN
  ☆14Updated 7 years ago
• neale / Adversarial-Autoencoder
  An adversarial autoencoder implementation in pytorch
  ☆83Updated 6 years ago
• JingWang18 / Discriminative-Feature-Alignment
  Discriminative Feature Alignment for Unsupervised Domain Adaptation
  ☆69Updated 4 years ago
• erlendd / ddan
  Deep domain adaptation networks (DDAN) library for Python with TensorFlow.
  ☆71Updated 5 years ago
• eli5168 / improved_gan_pytorch
  Pytorch implementation of semi-supervised DCGAN based on "Improved Techniques for Training GANs"
  ☆41Updated 3 years ago
• shucunt / domain_adaptation
  this project is the code of domain adaptation referenced by unsupervised domain adaptation by backpropagation(http://machinelearning.wust…
  ☆63Updated 7 years ago
• PramuPerera / OCGAN
  ☆51Updated 2 years ago
• ricvolpi / adversarial-feature-augmentation
  Code for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018
  ☆130Updated 6 years ago
• ljain2 / libsvm-openset
  ☆51Updated 10 years ago
• xinario / catgan_pytorch
  Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
  ☆61Updated 7 years ago
• EMRResearch / ExtremeValueMachine
  This is an implementation of the Extreme Value Machine by Rudd et al., with minor changes from the original work.
  ☆51Updated 6 years ago
• SchafferZhang / DADA
  code for "DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification"
  ☆50Updated 6 years ago
• wanglouis49 / pytorch-autoencoders
  Implementation of autoencoders in PyTorch
  ☆57Updated 7 years ago
• PramuPerera / TransferLearningNovelty
  ☆23Updated 5 years ago
• nitahhhh / adda-pytorch
  Implementation of "Adversarial Discriminative Domain Adaptation" on pytorch
  ☆31Updated 7 years ago
• asahi417 / DeepDomainAdaptation
  Tensorflow deep learning based domain adaptation model implementations with experiment of estimate MNIST by SVHN data (SVHN -> MNIST): DA…
  ☆26Updated 6 years ago
• pmorerio / minimal-entropy-correlation-alignment
  Code for the paper "Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation", ICLR 2018
  ☆63Updated 5 years ago
• yanzhicong / VAE-GAN
  Implemention of "CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training"
  ☆45Updated 6 years ago
• Vastlab / libMR
  Library for Meta-Recognition and Weibull based calibration of SVM data.
  ☆83Updated 2 years ago
• IssamLaradji / M-ADDA
  Domain Adaptation Based on the Triplet Loss
  ☆113Updated 5 years ago
• liudaizong / Residual-Attention-Network
  ☆39Updated 7 years ago
• thuml / MADA
  Code release for "Multi-Adversarial Domain Adaptation" (AAAI 2018)
  ☆58Updated 6 years ago
• ShayanPersonal / stacked-autoencoder-pytorch
  Stacked denoising convolutional autoencoder written in Pytorch for some experiments.
  ☆131Updated last year
• yogeshbalaji / Generate_To_Adapt
  Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"
  ☆142Updated 5 years ago
• chenchao666 / JDDA-Master
  Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation（AAAI-2019）
  ☆83Updated 5 years ago
• chenchao666 / HoMM-Master
  Official Code of Paper HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation (AAAI2020)
  ☆44Updated 5 years ago
• hellowangqian / domain-adaptation-capls
  Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
  ☆58Updated 3 years ago