145 Open source projects that are alternatives of or similar to MNIST-adversarial-images

Adversarial Discriminative Domain Adaptation in Chainer

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

Lingvo

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Generative Adversarial Networks in TensorFlow 2.0

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

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

手写数字识别。利用Tensorflow和MNIST实现的一个简单的手写数字识别程序。

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

TI-pooling: transformation-invariant pooling for feature learning in Convolutional Neural Networks

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A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset

A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

Generative Adversarial Network for MNIST with tensorflow

This program shows how Bounding-Box-Regression works in a visual form. Intersection over Union ( IOU ), Non Maximum Suppression ( NMS ), Object detection, 边框回归，边框回归可视化，交并比，非极大值抑制，目标检测。

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.

Tensorflow implementation of conditional variational auto-encoder for MNIST

Playing with MNIST. Machine Learning. Generative Models.

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

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Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch

Pytorch、Scikit-learn实现多种分类方法，包括逻辑回归（Logistic Regression）、多层感知机（MLP）、支持向量机（SVM）、K近邻（KNN）、CNN、RNN，极简代码适合新手小白入门，附英文实验报告（ACM模板）

A small convolution neural network deep learning framework implemented in c++.

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

Simple Implementation of many GAN models with PyTorch.

PyTorch implementation of Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN)

Minimalist implementation of VQ-VAE in Pytorch

Tensorflow implementation of adversarial auto-encoder for MNIST

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

A realtime digit OCR on the browser using Machine Learning

MNIST with TensorFlow Lite on Android

Tensorflow 2.0 implementation of Adversarial Autoencoders

Collection of generative models in Pytorch version.

Experiments on cluttered mnist dataset with Tensorflow.

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

Keras MNIST for Handwriting Detection

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

🎎 InfoGAN: Interpretable Representation Learning

Implementation of a digit recognition using my Neural Network with the MNIST data set.

This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference.

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

The Pytorch Implementation of L-Softmax

In-Browser Digit recognition with Tensorflow.js and React using Mnist dataset

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

Implementation of LeNet5 without any auto-differentiate tools or deep learning frameworks. Accuracy of 98.6% is achieved on MNIST dataset.

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Implementation of "Effective Adversarial Regularization for Neural Machine Translation", ACL 2019

Tensorflow implementation of different GANs and their comparisions

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

Adventures using keras on Google's Cloud ML Engine

Predict handwritten digits with CoreML

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

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Multi-Classification on dataset of MNIST

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