Conditional diffusion model to generate MNIST. Minimal script. Based on 'Classifier-Free Diffusion Guidance'.

A single handwritten digit classifier, using the MNIST dataset. Pure Numpy.

Implementation of CNN on ZYNQ FPGA to classify handwritten numbers using MNIST database

PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.

Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset.

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

Using spiking neurons and spike-timing-dependent plasticity to classify the MNIST handwritten digits.

Some samples of the MNIST classifier.

Classify MNIST image dataset into 10 classes. Build an image classifier with Recurrent Neural Network (RNN: LSTM) on Tensorflow.

A feature extractor based on Python 3, Tensorflow, and Scikit-learn created to improve the SVM accuracy to classify the MNIST dataset fast and with more accuracy.

Baseline classifiers on the polluted MNIST dataset, SJTU CS420 course project

Digit Classifier trained on MNIST and tested using webcam.

SmartGlass (SG) is a python implementation of a diffractive optical neural network. Currently, it supports training an all-optical classifier (e.g. classify hand-written digits MNIST dataset). Besides, the framework can also be used to design optics based on a task like focusing and beam steering. However, custom object functions should be defined. Besides training the optics, the SG also supports training the detectors using gradient-free optimization.

Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!

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

A simple implementation of classifier-free guidance DDIM on MNIST

Compression algorithms (like the well-known zip file compression) can be used for machine learning purposes, specifically for classifying hand-written digits (MNIST)

Kuzushiji-MNIST - Capsule Network, VGG and ResNet classifiers for the "lost" Japanese language dataset

MNIST classifier with average 0.17% error

PyTorch implementation of DDPM Demo and Classifier-Free DDPM Demo. Experiments were conducted on MNIST and Cifar10 datasets.

A sample project implementing TensorFlowSharp and a trained CNN network to classify MNIST images in the Unity Video Game Engine

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

Training a MNIST classifier, and use it to compute inception score (ICP)

MNIST classifier implementation with PyTorch Lightning on Amazon SageMaker

Interpretability methods applied on image classifiers trained on MNIST and CIFAR10

A simple webservice API for classifying MNIST digits from HTTP POST requests built using Flask and TensorFlow/Layer

PyTorch implementation of classifier for Fashion-MNIST

End to end training MNIST image classifier with tf.Keras, convert to TFLite and deploy to Android

论文 Hinton等的论文 Matrix capsules with EM routing - Hinton, G. E., Sabour, S. and Frosst, N. (2018) Dynamic Routing Between Capsules - Sabour, S., Frosst, N. and Hinton, G.E. (2017) Transforming Auto-encoders - Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011) A parallel computation that assigns canonical object-based frames of reference. - Hinton, G.E. (1981) Shape representation in parallel systems - Hinton, G.E. (1981) Optimizing Neural Networks that Generate Images - Tijmen Tieleman’s disseration 其他论文 Capsule Network Performance on Complex Data - Xi, E., Bing, S. and Jin, Y. (2017) Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture - Qiao, K., Zhang, C., Wang, L., Yan, B., Chen, J., Zeng, L. and Tong, L., (2018) An Optimization View on Dynamic Routing Between Capsules - Wang, D., Liu, E. (2018) CapsuleGAN: Generative Adversarial Capsule Network Ayush Jaiswal, Wael AbdAlmageed, Premkumar Natarajan. (2018) Spectral Capsule Networks - Bahadori, M. T. (2018) 博客 Max Pechyonkin的胶囊网络入门系列： 胶囊网络背后的直觉 胶囊如何工作 囊间动态路由算法 胶囊网络架构 基于TensorFlow实现胶囊网络 Debarko De的胶囊网络教程，包括注释详尽的胶囊网络实现代码 基于CUDA为胶囊网络实现TensorFlow定制操作 Jos van de Wolfshaar的文章，定制胶囊网络运算的CUDA支持 ISI新研究：胶囊生成对抗网络 用胶囊网络替换CNN作为GAN的判别网络，在MNIST数据集上取得了比卷积GAN更好的表现 Uncovering the Intuition behind Capsule Networks and Inverse Graphic Tanay Kothari的长篇教程 A Visual Representation of Capsule Connections in Dynamic Routing Between Capsules Mike Ross的胶囊网络示意图 Capsule Networks Are Shaking up AI — Here’s How to Use Them Nick Bourdakos的介绍 Capsule Networks Explained Kendrick Tan的解释 Understanding Dynamic Routing between Capsules (Capsule Networks) Jonathan Hui的教程，包括注释详尽的基于Keras的胶囊网络实现代码 Matrix capsules with EM routing Adrian Colyer关于EM路由的文章 Capsule Networks: A Glossary Sebastian Kwiatkowski的胶囊网络术语表 Overview of awesome articles 点评三篇胶囊网络教程 视频 Geoffrey Hinton’s talk: What is wrong with convolutional neural nets? - Geoffrey Hinton在MIT. Brain & Cognitive Sciences的演讲《卷积神经网络的问题在哪里？》 Capsule Networks (CapsNets) – Tutorial - “这视频棒极了。我本希望我能把胶囊解释得这么清楚。”Geoffrey Hinton Capsule networks: overview - 胶囊网络概览，包括向量和矩阵胶囊。 Overview of awesome videos 对以上3个视频的点评。 Capsule Networks: An Improvement to Convolutional Networks Siraj Raval介绍胶囊网络的视频 动态路由实现 官方实现 Sarasra/models 《Dynamic Routing Between Capsules》论文所用的代码 TensorFlow alisure-ml/CapsNet bourdakos1/capsule-networks etendue/CapsNet_TF InnerPeace-Wu/CapsNet-tensorflow jaesik817/adv_attack_capsnet jostosh/capsnet JunYeopLee/capsule-networks laodar/tf_CapsNet leoniloris/CapsNet naturomics/CapsNet-Tensorflow rrqq/CapsNet-tensorflow-jupyter thibo73800/capsnet-traffic-sign-classifier tjiang31/CapsNet winwinJJiang/capsNet-Tensorflow PyTorch acburigo/CapsNet adambielski/CapsNet-pytorch AlexHex7/CapsNet_pytorch aliasvishnu/Capsule-Networks-Notebook-MNIST andreaazzini/capsnet.pytorch cedrickchee/capsule-net-pytorch dragen1860/CapsNet-Pytorch gram-ai/capsule-networks higgsfield/Capsule-Network-Tutorial laubonghaudoi/CapsNet_guide_PyTorch leftthomas/CapsNet nishnik/CapsNet-PyTorch tonysy/CapsuleNet-PyTorch Ujjwal-9/CapsNet Keras fengwang/minimal-capsule gusgad/capsule-GAN mitiku1/Emopy-CapsNet ruslangrimov/capsnet-with-capsulewise-convolution streamride/CapsNet-keras-imdb sunxirui310/CapsNet-Keras theblackcat102/dynamic-routing-capsule-cifar XifengGuo/CapsNet-Keras XifengGuo/CapsNet-Fashion-MNIST Chainer soskek/dynamic_routing_between_capsules Torch mrkulk/Unsupervised-Capsule-Network MXNet AaronLeong/CapsNet_Mxnet GarrickLin/Capsnet.Gluon Soonhwan-Kwon/capsnet.mxnet CNTK Southworkscom/CapsNet-CNTK Lasagne DeniskaMazur/CapsNet-Lasagne Matlab yechengxi/LightCapsNet R dfalbel/capsnet JavaScript alseambusher/capsnet.js Vulcan moothyknight/CapsNet-for-Graphics-Rendering-Optimization EM路由实现 TensorFlow gyang274/capsulesEM www0wwwjs1/Matrix-Capsules-EM-Tensorflow PyTorch shzygmyx/Matrix-Capsules-pytorch 其他资源 Capsule Networks discussion Facebook讨论组 CapsNet-Tensorflow CapsNet-Tensorflow的gitter.im讨论组 Will capsule networks replace neural networks? Quora问答“胶囊网络会取代神经网络吗？” Could GANs work with Hinton’s capsule theory? Quora问答“GAN可以应用Hinton的胶囊理论吗？” Dynamic Routing Between Capsules Kyuhwan Jung对论文《Dynamic routing between Capsules》的评论（slideshare）

Vitis AI Lab: MNIST classifier