669 Open source projects that are alternatives of or similar to Variational Ladder Autoencoder

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

Ladder Variational Autoencoders (LVAE) in PyTorch

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

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]

DGMs for NLP. A roadmap.

Contrastive Predictive Coding for Automatic Speaker Verification

Unsupervised Representation Learning for Singing Voice Separation

Official repository for the ShapeFormer Project

Pytorch implementation of "A Simple Framework for Contrastive Learning of Visual Representations"

Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

Keras implementation of Representation Learning with Contrastive Predictive Coding

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

📜 Self-Supervised Learning from Images: Up-to-date reading list.

PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

Unsupervised learning and generative models in python/pytorch.

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Official adversarial mixup resynthesis repository

Proto-RL: Reinforcement Learning with Prototypical Representations

Vector Quantized Autoregressive Predictive Coding (VQ-APC)

This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link: https://arxiv.org/abs/2010.00029

Generative 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..

Experiments for understanding disentanglement in VAE latent representations

SCAN: Learning to Classify Images without Labels (ECCV 2020), incl. SimCLR.

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Python implementation of bag-of-concepts

A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).

A comprehensive list of awesome contrastive self-supervised learning papers.

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

PHP-ML - Machine Learning library for PHP

Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds (CVPR 2020)

Bayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

Neural Relation Understanding: neural cardinality estimators for tabular data

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Awesome Graph Self-Supervised Learning

An implementation of "Community Preserving Network Embedding" (AAAI 2017)

A tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

Unsupervised Machine Learning: Nonnegative Tensor Factorization + k-means clustering

Simplified version of "State Representation Learning for Control: An Overview" bibliography

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

A sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).

A stable algorithm for GAN training

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.

PixelVAE with or without regularization

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Autoregressive Predictive Coding: An unsupervised autoregressive model for speech representation learning

Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling

VIP cheatsheets for Stanford's CS 229 Machine Learning

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Code release for "Canonical Surface Mapping via Geometric Cycle Consistency"

[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition

R/shiny interface for interactive visualization of data in SummarizedExperiment objects

🎨 Color recognition & classification & detection on webcam stream / on video / on single image using K-Nearest Neighbors (KNN) is trained with color histogram features by OpenCV.

Video Frame Synthesis using Deep Voxel Flow (ICCV 2017 Oral)

Supplementary material for Hands-On Machine Learning with R, an applied book covering the fundamentals of machine learning with R.