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为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
pix2tex: Using a ViT to convert images of equations into LaTeX code.
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Curated list of apps and tools that not only use the new ChatGPT API, but also allow users to configure their own API keys, enabling free and on-demand usage of their own quota.
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
PyTorch implementation of Contrastive Learning methods
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
A Python Library for Graph Outlier Detection (Anomaly Detection)
A pytorch adversarial library for attack and defense methods on images and graphs
PyGCL: A PyTorch Library for Graph Contrastive Learning
[AAAI 2024 Oral] AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
Conditional diffusion model to generate MNIST. Minimal script. Based on 'Classifier-Free Diffusion Guidance'.
A pytorch library for graph and hypergraph computation.
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
The official PyTorch implementation of paper BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)
A simple and effective method for detecting out-of-distribution images in neural networks.
A Unified Python Library for Graph Prompting
Dataset Condensation (ICLR21 and ICML21)
MemAE for anomaly detection. -- Gong, Dong, et al. "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection". ICCV 2019.
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
Learning to Prompt (L2P) for Continual Learning @ CVPR22 and DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning @ ECCV22