307 Open source projects that are alternatives of or similar to pyroVED

VAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.

An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

IPCCC 2018: Robust and Unsupervised KPI Anomaly Detection Based on Conditional Variational Autoencoder

Text Variational Autoencoder inspired by the paper 'Generating Sentences from a Continuous Space' Bowman et al. https://arxiv.org/abs/1511.06349

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)

Implemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets

Tensorflow implementation of variational auto-encoder for MNIST

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

PyTorch implementation of "Auto-Encoding Variational Bayes"

Tensorflow implementation of conditional variational auto-encoder for MNIST

Assessing Generative Models via Precision and Recall (official repository)

A tensorflow implementation of "Generating Sentences from a Continuous Space"

[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"

Pytorch implementation of Hyperspherical Variational Auto-Encoders

There 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.

Collection of generative models in Tensorflow

No description or website provided.

Experiments for understanding disentanglement in VAE latent representations

Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch

Tensorflow implementation of Hyperspherical Variational Auto-Encoders

Stochastic Adversarial Video Prediction

A Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).

Protein function prediction using a variational autoencoder

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

music generation with a classifying variational autoencoder (VAE) and LSTM

Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Implementation of a convolutional Variational-Autoencoder model in pytorch.

Simple and Effective Face Detector, based on Progressive Calibration Networks (PCN) which is an accurate rotation-invariant face detector running at real-time speed on CPU, published in CVPR 2018.

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. (DEPRECATED)

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

[ WSDM '19 ] Sequential Variational Autoencoders for Collaborative Filtering

Implementation of the paper - Automatic chemical design using a data-driven continuous representation of molecules

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

No description or website provided.

Python codes for weakly-supervised learning

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Interactive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.

Generalized Product Quantization Network For Semi-supervised Image Retrieval - CVPR 2020

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

No description or website provided.

EfficientMORL (ICML'21)

PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"

Automatic Gap-Fill Question Generation

Playing with MNIST. Machine Learning. Generative Models.

Code for the paper: "SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification"

🧹 Formerly for binary classification with noisy labels. Replaced by cleanlab.

[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

Keras implementations of three language models: character-level RNN, word-level RNN and Sentence VAE (Bowman, Vilnis et al 2016).

If you've always wanted to learn about deep-learning but don't know where to start, then you might have stumbled upon the right place!

Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

PyTorch study

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning