Deep Learning With PythonExample projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
Stars: ✭ 134 (+436%)
Vae TensorflowA Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117 (+368%)
Pytorch VaeA CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch
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Tensorflow Mnist CvaeTensorflow implementation of conditional variational auto-encoder for MNIST
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Cada Vae PytorchOfficial implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)
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Cross Lingual Voice CloningTacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.
Stars: ✭ 106 (+324%)
Pytorch Vq VaePyTorch implementation of VQ-VAE by Aäron van den Oord et al.
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Pytorch RlThis repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
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Vae cfVariational autoencoders for collaborative filtering
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MojitalkCode for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
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Vae For Image GenerationImplemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets
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VdeVariational Autoencoder for Dimensionality Reduction of Time-Series
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BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
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classifying-vae-lstmmusic generation with a classifying variational autoencoder (VAE) and LSTM
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continuous BernoulliThere are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.
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S Vae PytorchPytorch implementation of Hyperspherical Variational Auto-Encoders
Stars: ✭ 255 (+920%)
Neural OdeJupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
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soft-intro-vae-pytorch[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Stars: ✭ 170 (+580%)
Generative models tutorial with demoGenerative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
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Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Stars: ✭ 418 (+1572%)
VAE-Gumbel-SoftmaxAn implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
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SmrtHandle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
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S Vae TfTensorflow implementation of Hyperspherical Variational Auto-Encoders
Stars: ✭ 198 (+692%)
srVAEVAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.
Stars: ✭ 56 (+124%)
Disentangling VaeExperiments for understanding disentanglement in VAE latent representations
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Vae Cvae MnistVariational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
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Joint VaePytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
Stars: ✭ 404 (+1516%)
Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
Stars: ✭ 422 (+1588%)
Tf VqvaeTensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
Stars: ✭ 226 (+804%)
benchmark VAEUnifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Stars: ✭ 1,211 (+4744%)
vae-concreteKeras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
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pyroVEDInvariant representation learning from imaging and spectral data
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MIDI-VAENo description or website provided.
Stars: ✭ 56 (+124%)
Dsprites DatasetDataset to assess the disentanglement properties of unsupervised learning methods
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Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (+1652%)
Variational AutoencoderVariational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
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Spml4dmStatistics and Machine Learning for Data Mining
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Nano281Data Science for Materials Science
Stars: ✭ 24 (-4%)