445 Open source projects that are alternatives of or similar to catseye

Learning about Haskell with Variational Autoencoders

Tensorflow implementation of conditional variational auto-encoder for MNIST

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Open Set Recognition

Tensorflow implementation of variational auto-encoder for MNIST

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Tensorflow 2.0 implementation of Adversarial Autoencoders

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

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

A curated list of dedicated resources and applications

Neural networks library for machine learning on PHP

Tensorflow implementation of adversarial auto-encoder for MNIST

Realtime rectangle detector with GPGPU

Number detection implemented using TensorFlow with custom CNN architecture for fast inference and custom dataset

Multi-Classification on dataset of MNIST

SE-Net Incorporates with ResNet and WideResnet on CIFAR-10/100 Dataset.

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

Convolutional AutoEncoder application on MRI images

DNN Reports module provides a simple, flexible, view on your database, any table, any view and display results with plugin visualizers

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

SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion

Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).

Real time face landmarking using decision trees and NN autoencoders

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

Implementations of machine learning algorithm by Python 3

用opencv的dnn模块实现人脸口罩检测，包含C++和Python两种版本的实现

A WebGPU Engine for real-time rendering and GPGPU

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

Training Low-bits DNNs with Stochastic Quantization

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

Create adversarial images to fool a MNIST classifier in TensorFlow

Dual CNN Models for Unsupervised Monocular Depth Estimation

STONNE: A Simulation Tool for Neural Networks Engines

A gui for tesseractOCR with some preprocessing image options (OpenCV) for improve character recognition

Handwritten text to synths!

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

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

A Pytorch implementation of "Deep Convolutional Generative Adversarial Networks"

The OpenCL Utility library

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

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…

Python＆機械学習ライブラリ TensorFlow の使い方の練習コード集。特にニューラルネットワークを重点的に取り扱い。

Small library for running arbitrary computations on the GPU, using JavaScript or WebAssembly.

Experiments on cluttered mnist dataset with Tensorflow.

Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

Modify from https://github.com/JarveeLee/SynthText_Chinese_version.git with python3 and cv3.

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

Deep learning image autoencoder that not depends on image resolution

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

Amplifier allows .NET developers to easily run complex applications with intensive mathematical computation on Intel CPU/GPU, NVIDIA, AMD without writing any additional C kernel code. Write your function in .NET and Amplifier will take care of running it on your favorite hardware.

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

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

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

A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

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

A realtime digit OCR on the browser using Machine Learning

Algorithms implemented in CUDA + resources about GPGPU

One-Shot Learning with Triplet CNNs in Pytorch