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

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.

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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.

transferlearning for small training set object detection

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

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

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

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