A best practice for deep learning project template architecture.

Segment Anything in Medical Images

Example code for Fluent Python, 2nd edition (O'Reilly 2022)

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

A PyTorch Library for Meta-learning Research

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbox).

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Anomaly Detection via Reverse Distillation from One-Class Embedding

Must-read Papers on Physics-Informed Neural Networks.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

H2O.ai Machine Learning Interpretability Resources

Interpretability Methods for tf.keras models with Tensorflow 2.x

Official Implementation of "E pluribus unum interpretable convolutional neural networks"