Official implementation of CVPR 2024 PromptAD: Learning Prompts with Only Normal Samples for Few-Shot Anomaly Detection
-
Updated
May 23, 2024 - Python
Official implementation of CVPR 2024 PromptAD: Learning Prompts with Only Normal Samples for Few-Shot Anomaly Detection
总结Prompt&LLM论文,开源数据&模型,AIGC应用
This code is for the honour thesis developed by Dannong Xu. It includes CTNet (developed algorithm in the thesis), Siamese Network, MAML, and Reptile.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)
Official implementation of the paper 'Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery'
FSL-Mate: A collection of resources for few-shot learning (FSL).
LLM projects
The official repo for the extension of [NeurIPS'22] "APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking": https://github.com/pandorgan/APT-36K
A collection of AWESOME things about domian adaptation
[NeurIPS 2023] The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
Study and review papers of journals and conferences.
Meta-Transformer: A meta-learning framework for scalable automatic modulation classification
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
An Effective Topic Modeling Approach under Resource-Limited Scenarios
LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.
The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.
Add a description, image, and links to the few-shot-learning topic page so that developers can more easily learn about it.
To associate your repository with the few-shot-learning topic, visit your repo's landing page and select "manage topics."