128 Open source projects that are alternatives of or similar to Keras Autoencoders

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

[EMNLP 2020] "T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack" by Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li

Variational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder

several basic neural networks[mlp, autoencoder, CNNs, recurrentNN, recursiveNN] implements under several NN frameworks[ tensorflow, pytorch, theano, keras]

Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

Keras implementation of Structural Deep Network Embedding, KDD 2016

MiraPy: A Python package for Deep Learning in Astronomy

Advanced Deep Learning with Keras, published by Packt

DanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)

Learning about Haskell with Variational Autoencoders

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

Torch implementations of various types of autoencoders

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

Autoencoder for Point Clouds

Autoencoders using Keras

Tensorflow implementation : U-net and FCN with global convolution

Video lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf

Automatic Feature Generation for Website Fingerprinting

Variational autoencoder, denoising autoencoder and other variations of autoencoders implementation in keras

Use unsupervised and supervised learning to predict stocks

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Next RecSys Library

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

AE and VAE Playground in PyTorch

Implementation of CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM paper (https://arxiv.org/pdf/1804.00874.pdf)

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

This repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)

Tensorflow implementation of variational auto-encoder for MNIST

The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

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

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

Frame level anomaly detection and localization in videos using auto-encoders

[CVPR2020] Adversarial Latent Autoencoders

[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoisi…

Large scale training of factorization models for Collaborative Filtering with PyTorch

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

The implementation of "Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence"

ICVGIP' 18 Oral Paper - Classification of thoracic diseases on ChestX-Ray14 dataset

PyTorch Blog Post On Image Similarity Search

Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.

PyTorch implementation of 'An Unsupervised Neural Attention Model for Aspect Extraction' by He et al. ACL2017'

Using Deep Autoencoders for predictions of movie ratings.

📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈

TensorFlow and Deep Learning Tutorials

Experiments with Adversarial Autoencoders using Keras

PyTorch implementation of SDAE (Stacked Denoising AutoEncoder)

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Structural Deep Clustering Network

A Pytorch implementation for the ZeroSpeech 2019 challenge.

Molecular AutoEncoder in PyTorch

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.