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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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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Tf VqvaeTensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
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Vae TensorflowA Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
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Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
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srVAEVAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.
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Pytorch VaeA CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch
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Deep Learning With PythonExample projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
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DraganA stable algorithm for GAN training
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Neuralnetworks.thought ExperimentsObservations and notes to understand the workings of neural network models and other thought experiments using Tensorflow
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Li emnlp 2017Deep Recurrent Generative Decoder for Abstractive Text Summarization in DyNet
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Pytorch Vq VaePyTorch implementation of VQ-VAE by Aäron van den Oord et al.
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MIDI-VAENo description or website provided.
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benchmark VAEUnifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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eccv16 attr2imgTorch Implemention of ECCV'16 paper: Attribute2Image
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pyroVEDInvariant representation learning from imaging and spectral data
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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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InpaintNetCode accompanying ISMIR'19 paper titled "Learning to Traverse Latent Spaces for Musical Score Inpaintning"
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char-VAEInspired by the neural style algorithm in the computer vision field, we propose a high-level language model with the aim of adapting the linguistic style.
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generative deep learningGenerative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
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VdeVariational Autoencoder for Dimensionality Reduction of Time-Series
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S Vae PytorchPytorch implementation of Hyperspherical Variational Auto-Encoders
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Neuraldialog CvaeTensorflow Implementation of Knowledge-Guided CVAE for dialog generation ACL 2017. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
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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"
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First Order ModelThis repository contains the source code for the paper First Order Motion Model for Image Animation
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AC-VRNNPyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"
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BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
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vae-concreteKeras implementation of a Variational Auto Encoder with a Concrete Latent Distribution
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style-vaeImplementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction
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vqvae-2PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"
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vae-torchVariational autoencoder for anomaly detection (in PyTorch).
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Pytorch RlThis repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
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classifying-vae-lstmmusic generation with a classifying variational autoencoder (VAE) and LSTM
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Dsprites DatasetDataset to assess the disentanglement properties of unsupervised learning methods
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Disentangling VaeExperiments for understanding disentanglement in VAE latent representations
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Joint VaePytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
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CHyVAECode for our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)
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Vae cfVariational autoencoders for collaborative filtering
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DiffuseVAEA combination of VAE's and Diffusion Models for efficient, controllable and high-fidelity generation from low-dimensional latents
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Generative ModelsCollection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
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Sentence VaePyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349
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Variational AutoencoderVariational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
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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
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Cross Lingual Voice CloningTacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.
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PsganPeriodic Spatial Generative Adversarial Networks
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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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Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
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Discogan PytorchPyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
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