A collection of AWESOME things about domain adaptation

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Chinese-LLaMA 1&2、Chinese-Falcon 基础模型；ChatFlow中文对话模型；中文OpenLLaMA模型；NLP预训练/指令微调数据集

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

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)

A curated list of papers, code and resources pertaining to zero shot learning

Official PyTorch implementation of ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [CVPR 2023 Highlight]

A curated list of prompt-based paper in computer vision and vision-language learning.

A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.

Official PyTorch implementation of GroupViT: Semantic Segmentation Emerges from Text Supervision, CVPR 2022.

A curated list of awesome prompt/adapter learning methods for vision-language models like CLIP.

汇聚【Python应用】【Python实训】【Python技术分享】等等

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.

PromptCLUE, 全中文任务支持零样本学习模型

[ICCV 2023] A latent space for stochastic diffusion models

Crosslingual Generalization through Multitask Finetuning

pCLUE: 1000000+多任务提示学习数据集

Diffusion Classifier leverages pretrained diffusion models to perform zero-shot classification without additional training

Pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper

Zero and Few shot named entity & relationships recognition

Awesome Domain Adaptation Python Toolbox

✨ Bootstrap annotation with zero- & few-shot learning via OpenAI GPT-3

Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019

[NeurIPS 2023] This repository includes the official implementation of our paper "An Inverse Scaling Law for CLIP Training"

[ECIR'24] Implementation of "Large Language Models are Zero-Shot Rankers for Recommender Systems"

Official implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Zero-Shot Learning part)

Generalist and Lightweight Model for Relation Extraction (Extract any relationship types from text)

CVPR2022, BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning, https://arxiv.org/abs/2203.01522