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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nvaeAn unofficial toy implementation for NVAE 《A Deep Hierarchical Variational Autoencoder》
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S Vae PytorchPytorch implementation of Hyperspherical Variational Auto-Encoders
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molecular-VAEImplementation of the paper - Automatic chemical design using a data-driven continuous representation of molecules
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Neural OdeJupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
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Pytorch-RL-CPPA Repository with C++ implementations of Reinforcement Learning Algorithms (Pytorch)
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Joint VaePytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
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pyroVEDInvariant representation learning from imaging and spectral data
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disent🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib
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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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concept-based-xaiLibrary implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI
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PycadlPython package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"
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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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Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
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Beta VaePytorch implementation of β-VAE
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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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probabilistic nlgTensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).
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Deepnude An Image To Image TechnologyDeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络(GAN,Generative Adversarial Network)图像生成的理论与实践研究。
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tensorflow-mnist-AAETensorflow implementation of adversarial auto-encoder for MNIST
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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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sqairImplementation of Sequential Attend, Infer, Repeat (SQAIR)
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generative deep learningGenerative Deep Learning Sessions led by Anugraha Sinha (Machine Learning Tokyo)
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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 (+1985.71%)
Parallel-Tacotron2PyTorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
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Dsprites DatasetDataset to assess the disentanglement properties of unsupervised learning methods
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TensorMONKA collection of deep learning models (PyTorch implemtation)
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Generative ModelsCollection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
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style-vaeImplementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction
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Pytorch rvaeRecurrent Variational Autoencoder that generates sequential data implemented with pytorch
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Carla-ppoThis repository hosts a customized PPO based agent for Carla. The goal of this project is to make it easier to interact with and experiment in Carla with reinforcement learning based agents -- this, by wrapping Carla in a gym like environment that can handle custom reward functions, custom debug output, etc.
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Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
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VAENAR-TTSPyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
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DaisyrecA developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks
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Advanced Models여러가지 유명한 신경망 모델들을 제공합니다. (DCGAN, VAE, Resnet 등등)
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Generative-ModelRepository for implementation of generative models with Tensorflow 1.x
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classifying-vae-lstmmusic generation with a classifying variational autoencoder (VAE) and LSTM
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Disentangling VaeExperiments for understanding disentanglement in VAE latent representations
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vae captioningImplementation of Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
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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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Tensorflow Vae Gan DrawA collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).
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BagelIPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder
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ladder-vae-pytorchLadder Variational Autoencoders (LVAE) in PyTorch
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Pytorch VqvaeVector Quantized VAEs - PyTorch Implementation
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learning-to-drive-in-5-minutesImplementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
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Variational AutoencoderVariational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
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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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Pytorch RlThis repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
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contiguous-succotashRecurrent Variational Autoencoder with Dilated Convolutions that generates sequential data implemented in pytorch
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