Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

这是一个基于Cloudflare实现的Telegram消息转发机器人，专注于将用户消息安全、高效地转发到后台群组每个用户独立群组中的分话题，直接话题中发送信息而无需再艾特回复，适用于客服、社区管理等场景。

Transfer Learning for Anime Characters Recognition

A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework

This repository is the official implementation Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

Tensorflow codes for ICML2018, Learning Semantic Representations for Unsupervised Domain Adaptation

To Detect and Classify Brain Tumors using CNN and ANN as an asset of Deep Learning and to examine the position of the tumor.

Fall Detection and Prediction using GRU and LSTM with Transfer Learning

Automatic Annotation tool for labelling images in bulk with their corresponding bounding box annotations.

Resources of domain adaptation papers on sentiment analysis that have used Amazon reviews

Dataset and code for "Interaction Attention Transfer Network for Cross-domain Sentiment Classification“

Velodrome combines semi-supervised learning and out-of-distribution generalization (domain generalization) for drug response prediction and pharmacogenomics

Interpretation of RNAseq experiments through robust, efficient comparison to public databases

A PyTorch implementation of Parameter-sharing Capsule Network based on the paper "Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification"

Simple CNN is a library that can be used to train and infer CNN models by use of PyTorch and ONNX.

Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting: CS229 Final Project

transferlearning for small training set object detection

Implementation of various basic layers forward and back propagation. CS 231n Stanford Spring 2018: Convolutional Neural Networks for Visual Recognition. Solutions to Assignments

Brain tumor detection using deep learning cnn and transfer learning and also build an app using fastapi

Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.

건국대학교 주관 행동모사 자율주행 경진대회 1위 (2024.06)

A collection of code snippets covering the basic fundamentals of PyTorch, including tensors, autograd, neural networks, and optimization.

Working repository for Computer Vision course 2018.

Apply deep learning to detect and classify cracks. Prior to tensorflow framework and developed a GUI for deployment purposes.

This project focuses on creating a multi-label classification system to categorize clothing images based on type (T-shirt or Hoodie) and color (Red, Yellow, Blue, Black, White). By leveraging advanced deep learning techniques, this solution aims to streamline product categorization for fashion e-commerce platforms like Matos Fashion.

Android Application with Tensorflow Backend for Plant Image Classification

This project involves the use of deep learning models to classify Indonesian batik motifs. By utilizing deep learning techniques such as Convolutional Neural Networks (CNNs) and Transfer Learning, this project aims to automatically identify three well-known batik motifs: Parang, Mega Mendung, and Kawung.

No description provided.

Skin cancer classification using Transfer Learning and explainable AI

This project classifies fruit images into four categories: Acai, Acerola, Apple, and Avocado. Using Convolutional Neural Networks (CNN), it predicts fruit types with VGG-16. The model is optimized with data augmentation, dropout, and batch normalization for better performance and accuracy.