312 Open source projects that are alternatives of or similar to Cifar-Autoencoder

Emerging Technologies Project - 4th Year 2017

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

Predict CIFAR-10 labels with 88% accuracy using keras.

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

MNIST classifier built for TensorFlow Lite - Android, iOS and other "lite" platforms

Invert the color of MNIST images with PyTorch

Multi-omics Autoencoder Integration: Deep learning-based heterogenous data analysis toolkit

This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated

This repository provides a Colab Notebook that shows how to use Spatial Transformer Networks inside CNNs in Keras.

GBDT (Gradient Boosted Decision Tree: 勾配ブースティング) のpythonによる実装

eForest: Reversible mapping between high-dimensional data and path rule identifiers using trees embedding

PC-DARTS (PC-DARTS: Partial Channel Connections for Memory-Efficient Differentiable Architecture Search, published in ICLR 2020) implemented in Tensorflow 2.0+. This is an unofficial implementation.

Build deep learning applications in a new and easy way.

Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"

ResNet for Cifar10

Autoencoder trained on ImageNet Using Torch 7

A numpy based CNN implementation for classifying images

Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)

Adversarial Discriminative Domain Adaptation in Chainer

A new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole…

Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

Deep learning image autoencoder that not depends on image resolution

This is the official implementation for IVA '19 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation".

A realtime digit OCR on the browser using Machine Learning

Simple Keras implementation of Triplet-Center Loss on the MNIST dataset

Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.

Shared Latent Space VAE's

Multi-Classification on dataset of MNIST

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

The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"

Neural nets for MNIST classification, simple single layer NN, 5 layer FC NN and convolutional neural networks with different architectures

A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

Using various CNN techniques on the MNIST dataset

Unsupervised (Self-Supervised) Clustering of Seismic Signals Using Deep Convolutional Autoencoders

Octave convolution

Bottleneck Transformers for Visual Recognition

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

Hands-on in-person workshop for Deep Learning with TensorFlow

Colorizing B&W Photos with Neural Networks

No description or website provided.

MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.

This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"

One-Shot Learning with Triplet CNNs in Pytorch

Tensorflow implementation for Active Shift Layer(ASL)

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

The simplest machine learning library for launching UIs, running evaluations, and comparing model performance.

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

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

Extreme Learning Machine implemented in Pytorch

A Flask web app for handwritten digit recognition using machine learning

Image retrieval program made in Tensorflow supporting VGG16, VGG19, InceptionV3 and InceptionV4 pretrained networks and own trained Convolutional autoencoder.

A implementation of DCGAN (Deep Convolutional Generative Adversarial Networks) for CIFAR10 image

Recognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.

Experiments with Adversarial Autoencoders using Keras

Using Bayesian Optimization to optimize hyper parameter in Keras-made neural network model.

Tensorflow-example：使用MNIST训练模型，并识别手写数字图片

Interactive Visual Machine Learning Demos.

anomaly detection by one-class SVM

This is the official implementation for the paper 'AutoEncoder by Forest'