weijiaheng

ChatGLM-6B

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

cleanlab

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

pyod

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

snorkel

A system for quickly generating training data with weak supervision

docta

A Doctor for your data

Awesome-Anything

General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX

hallucination-leaderboard

Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents

TextGAN-PyTorch

TextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.

DiffuSeq

[ICLR'23] DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models

ActGPT

chatbot does what you ask, like open Google search, post a Tweet, etc.

awesome-demos

links and status of cool gradio demos

wrench

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

llm_unlearn

LLM Unlearning

SimiFeat

CAL

A Second-Order Approach to Learning with Instance-Dependent Label Noise (CVPR'21 oral)

DuelGAN

PyTorch Implementation of ECCV 2022 paper: "DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training".

Awesome-3D-AnomalyDetection

Awesome papers on 3D anomaly detection.

Drops

[ICLR 2023] Official Tensorflow implementation of "Distributionally Robust Post-hoc Classifiers under Prior Shifts"

MRMGA4VAD

[ICDM 2022] Making Reconstruction-based Method Great Again for Video Anomaly Detection (PyTorch)

SLA2P

[CIKM 2022] Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection (PyTorch)

HOC

LC

Official Implementation of Avoiding spurious correlations via logit correction

SelfSup_NoisyLabel

Learning from noisy labels via regularization between representations

IJCAI-tutorial

SelfSup_NoisyLabel

fair-eval

FairInfl

Fairness_Transferability

Fairness Transferability subject to Distribution Shift

gradio

Create UIs for your machine learning model in Python in 3 minutes

google-research

Google Research