📚 Supported Methods¶

🌱 Class-Incremental Learning¶

Method	Paper	Venue
`Joint`	update models using all the data from all classes
`Finetune`	baseline method which simply updates model using current data
`LwF`	Learning without Forgetting	ECCV 2016
`EWC`	Overcoming catastrophic forgetting in neural networks	PNAS 2017
`iCaRL`	Incremental Classifier and Representation Learning	CVPR 2017
`BiC`	Large Scale Incremental Learning	CVPR 2019
`WA`	Maintaining Discrimination and Fairness in Class Incremental Learning	CVPR 2020
`DER`	Dynamically Expandable Representation for Class Incremental Learning	CVPR 2021
`Coil`	Co-Transport for Class-Incremental Learning	ACM MM 2021
`GEM`	Gradient Episodic Memory for Continual Learning	NIPS 2017
`SSRE`	Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning	CVPR 2022
`FOSTER`	Feature Boosting and Compression for Class-incremental Learning	ECCV 2022
`FeTrIL`	Feature Translation for Exemplar-Free Class-Incremental Learning	WACV 2023
`MEMO`	Memory-Efficient Class-Incremental Learning	ICLR 2023

Method	Paper	Venue
`Joint`	update models using all the data from all classes
`L2P`	Learning to Prompt for Continual Learning	CVPR 2022
`DualPrompt`	DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning	ECCV 2022
`CODA-Prompt`	CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning	CVPR 2023
`S-Prompt`	S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning	NeurIPS 2022

Method	Paper	Venue
`Alice`	Few-Shot Class-Incremental Learning from an Open-Set Perspective	ECCV 2022
`FACT`	Forward Compatible Few-Shot Class-Incremental Learning	CVPR 2022
`SAVC`	Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning	CVPR 2023

Method	Paper	Venue
`MSP`	A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	ICLR 2017
`ODIN`	Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks	ICLR 2018
`MDS`	A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks	NeurIPS 2018
`MLS`	Scaling Out-of-Distribution Detection for Real-World Settings	ICML 2022
`ViM`	Out-Of-Distribution with Virtual-logit Matching	CVPR 2022
`FDBD`	Fast Decision Boundary based Out-of-Distribution Detector	ICML 2024
`VOS`	Learning What You Don't Know by Virtual Outlier Synthesis	ICLR 2022
`LogitNorm`	Mitigating Neural Network Overconfidence with Logit Normalization	ICML 2022
`DML`	Decoupling MaxLogit for Out-of-Distribution Detection	CVPR 2023

Method	Paper	Venue
`MCM`	Delving into Out-of-Distribution Detection with Vision-Language Representations	NeurIPS 2022
`GLMCM`	Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection	IJCV 2025
`CoOp`	Learning to Prompt for Vision-Language Models	IJCV 2022
`LoCoOp`	Few-Shot Out-of-Distribution Detection via Prompt Learning	NeurIPS 2023
`SCT`	Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection	NeurIPS 2024
`DPM`	Vision-Language Dual-Pattern Matching for Out-of-Distribution Detection	ECCV 2024

TBD

TBD