Popular GitHub Repositories for Catastrophic Overfitting

Notes for Deep Learning Specialization Courses led by Andrew Ng.

A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

PyContinual (An Easy and Extendible Framework for Continual Learning)

An Extensible Continual Learning Framework Focused on Language Models (LMs)

JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

unofficial re-implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets"

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community ? and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting ? All while having fun learning and participating in our Deep Learning Trivia games ? http://bit.ly/deep-learning-tf

The code repository for the CURLoRA research paper. Stable LLM continual fine-tuning and catastrophic forgetting mitigation.

CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for task-aware parameter-efficient fine-tuning(NeurIPS 2024)

Pre-training and Lifelong learning for User Embedding and Recommender System

PILF: A IPWT-inspired continual learning experiment focus on mitigate catastrophic forgetting with Surprise-gated Mixture of Experts (MoE) model.

Code for our NeurIPS′24 paper

Source code for "Online Unsupervised Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions", ECCV 2022. This is the code has been implemented to perform training and evaluation of UDA approaches in continuous scenarios. The library has been implemented in PyTorch 1.7.1. Some newer versions should work as well.

Understanding Catastrophic Overfitting in Single-step Adversarial Training [AAAI 2021]

papers of universal user representation learning for recommendation

ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

The official GitHub repo for the paper ""Nuclear Deployed!": Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents"

Awesome Incremental / Continual / Lifelong Generative Learning