Python Convolutional-autoencoders Resources

Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training [Arxiv] VideoMAE: Masked Autoencoders are Data-Efficient Learne

MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo

Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) This is the official implementation of Focals Conv (CVPR 2022), a new sp

MTFAA-Net Unofficial PyTorch implementation of Baidu's MTFAA-Net: "Multi-Scale Temporal Frequency Convolutional Network With Axial Attention for Speec

ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M

Intrusion-Detection-System-Using-CNN-and-Transfer-Learning This is the code for the paper entitled "A Transfer Learning and Optimized CNN Based Intrus

Project summary This repository contains the implementation of my bachelor degree project. The aim of the project is to apply non-linear feature reduc

YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di

Traffic-Sign-Recognition In this report, we propose a Convolutional Neural Network(CNN) for traffic sign classification that achieves outstanding perf

ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo

Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt

OREPA: Online Convolutional Re-parameterization This repo is the PyTorch implementation of our paper to appear in CVPR2022 on "Online Convolutional Re

PyOpenVINO - An Experimental Python Implementation of OpenVINO Inference Engine (minimum-set) Description The PyOpenVINO is a spin-off product from my

C-CNN: Contourlet Convolutional Neural Networks This repo implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networ

Convolutional Neural Network to detect deforestation in the Amazon Rainforest This project is part of my final work as an Aerospace Engineering studen

Multi-Stage Spatial-Temporal Convolutional Neural Network (MS-GCN) This code implements the skeleton-based action segmentation MS-GCN model from Autom

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition Paper: MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition accepted fo

DiffStride is a pooling layer with learnable strides. Unlike strided convolutions, average pooling or max-pooling that require cross-validating stride values at each layer, DiffStride can be initialized with an arbitrary value at each layer (e.g. (2, 2) and during training its strides will be optimized for the task at hand.

TDY-CNN for Text-Independent Speaker Verification Official implementation of Temporal Dynamic Convolutional Neural Network for Text-Independent Speake

Probabilistic U-Net + **Update** + An improved Model (the Hierarchical Probabilistic U-Net) + LIDC crops is now available. See below. Re-implementatio

pytorch-semseg Semantic Segmentation Algorithms Implemented in PyTorch This repository aims at mirroring popular semantic segmentation architectures i

keras-semantic-segmentation-example Example of semantic segmentation in Keras Single class example: Generated data: random ellipse with random color o

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternative libraries that can be used for this purpose, one of which is the PyTorch library.

This project is a convolutional neural network (CNN) that analyzes self-generated images in a variety of languages to find etymological similarities. Specifically, the goal is to prove that computer vision can be used to identify cognates known to exist, and perhaps lead linguists to evidence of unknown cognates.

Description This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs) in [Gardy et

traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to

Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 The repository provides 100+ Pap

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

This is a Kaggle competition in which we have to identify if the given lesion image is malignant or not for Melanoma which is a type of skin cancer.

SegPC-2021 This is the official repository for the ISBI 2021 paper Transformer Assisted Convolutional Neural Network for Cell Instance Segmentation by

Awesome Semantic Segmentation List of awesome things around semantic segmentation 🎉 Semantic segmentation is a computer vision task in which we label

Lung Segmentation (2D) Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images. Demo See the application of the

Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of

ild-cnn This is supplementary material for the manuscript: "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neur

Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA). Master's thesis documents. Bibliography, experiments and reports.

This repo has been deprecated because whole things are re-implemented by using Chainer and I did refactoring for many codes. So please check this newe

This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi

self-driving-car In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree. Hope this might

Build status: master (production branch): develop (development branch): Welcome to the CN24 GitHub repository! CN24 is a complete semantic segmentatio

Segment axon and myelin from microscopy data using deep learning. Written in Python. Using the TensorFlow framework. Based on a convolutional neural network architecture. Pixels are classified as either axon, myelin or background.

[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas

VarDACAE This module is used to create Convolutional AutoEncoders for Variational Data Assimilation. A user can define, create and train an AE for Dat

Biomedical Entity Linking This repo provides the code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Res

Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng

TCNN Pandey A, Wang D L. TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain[C]//ICASSP 2019-2019 IEEE Int

Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment

EfficientWord-Net Hotword detection based on one-shot learning Home assistants require special phrases called hotwords to get activated (eg:"ok google

BAM and CBAM Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)" Updat

JAX Models Table of Contents About The Project Getting Started Prerequisites Installation Usage Contributing License Contact About The Project The JAX

FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt

TransferNet: Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network Created by Seunghoon Hong, Junhyuk Oh,

Weakly Supervised Segmentation with TensorFlow This repo contains a TensorFlow implementation of weakly supervised instance segmentation as described

Part Detector Discovery This is the code used in our paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodne

Food recognition model using convolutional neural network & computer vision. The goal is to match or beat the DeepFood Research Paper

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks arXiv preprint: https://arxiv.org/abs/2201.02143. Architec

The dynamics of representation learning in shallow, non-linear autoencoders The package is written in python and uses the pytorch implementation to ML

Overview This Python package provides implementations of Augmented Principal Component Analysis (AugmentedPCA) - a family of linear factor models that

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

🎉TensorFlow2.0-Examples🎉! "Talk is cheap, show me the code." ----- Linus Torvalds Created by YunYang1994 This tutorial was designed for easily divin

Anime4K Anime4K is a set of open-source, high-quality real-time anime upscaling/denoising algorithms that can be implemented in any programming langua

Masked Autoencoders: A PyTorch Implementation This is a PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners: @

FlatGCN This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation, submitted to ICASSP2022). Req

Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin

Deep Learning for Time Series Classification This is the companion repository for our paper titled "Deep learning for time series classification: a re

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

ConvLSTM_pytorch This file contains the implementation of Convolutional LSTM in PyTorch made by me and DavideA. We started from this implementation an

DECA Official code for the ICCV 2021 paper "DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders". All the code is writte

AlexNet training on ImageNet LSVRC 2012 This repository contains an implementation of AlexNet convolutional neural network and its training and testin

Convolutional Neural Networks for Sentence Classification Code for the paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). R

U-Net Implementation By Christopher Ley This is my interpretation and implementation of the famous paper "U-Net: Convolutional Networks for Biomedical

Image 🖼 Classification App Boilerplate Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and ho

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

Net2Net Code accompanying the NeurIPS 2020 oral paper Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach*, Patri

Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

Adversarial Autoencoders (AAE) Tensorflow implementation of Adversarial Autoencoders (ICLR 2016) Similar to variational autoencoder (VAE), AAE imposes

DCGAN in Tensorflow Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networ

Deep Inside Convolutional Networks This is a caffe implementation to visualize the learnt model. Part of a class project at Georgia Tech Problem State

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

Convolutional Two-Stream Network Fusion for Video Action Recognition

Deep Learning - Image Captioning with Convolutional and Recurrent Neural Nets ========================================================================

Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 50+ Papers across Computer Visio

Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logically and chronologically related shots taken in a specific order to depict an over-arching concept or story.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers

Coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar

Saliency Methods 🔴 Now framework-agnostic! (Example core notebook) 🔴 🔗 For further explanation of the methods and more examples of the resulting ma

Tensorflow Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributi

Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend This project acts as both a tuto

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

DGI Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341 Overview Here we provide an implementation of Deep Graph Infom

Block Modeling-Guided Graph Convolutional Neural Networks This repository contains the demo code of the paper: Block Modeling-Guided Graph Convolution

deeper_insights_into_GCNs Deeper insights into graph convolutional networks for semi-supervised learning References data and utils.py come from Implem

Reference Code for AAAI-20 paper "Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels" Please refer to htt