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class-incremental learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/abdohelmy/D-3Former

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D3Former: Debiased Dual Distilled Transformer for Incremental Learning

https://github.com/adekhovich/continual_prune_and_select

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Continual Prune-and-Select: Class-incremental learning with specialized subnetworks

https://github.com/adityashahane10/gotham--graph-based-class-incremental-learning-framework-under-weak-supervision

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GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision

https://github.com/ahmadisahar/accv_fcil3d

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Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor

https://github.com/aignlai/gddsg

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Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping

https://github.com/aimagelab/mammoth

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Dark Experience for General Continual Learning: a Strong, Simple Baseline

https://github.com/albinsou/ocl_survey

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A Comprehensive Empirical Evaluation on Online Continual Learning

https://github.com/aliayub7/CBCL

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Cognitively-Inspired Model for Incremental Learning Using a Few Examples

https://github.com/aliayub7/cbcl-pr

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CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics

https://github.com/alviur/ldc

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Exemplar-free Continual Representation Learning via Learnable Drift Compensation

https://github.com/amazon-research/sp-cil

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Class-Incremental Learning with Strong Pre-trained Models

https://github.com/anwarmaxsum/pip

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PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning

https://github.com/arvin0313/mecoin-gfscil

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An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning

https://github.com/ashok-arjun/CSCCT

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Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer

https://github.com/bigdata-inha/Split-and-Bridge

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Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network

https://github.com/biomedia-mbzuai/dynammo

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DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images

https://github.com/blackblitz/bcl

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On Sequential Loss Approximation for Continual Learning

https://github.com/braincog-x/brain-cog

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Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks

https://github.com/burakgurbuz97/sharp-continual-learning

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SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning

https://github.com/bwnzheng/lpa_icme2023

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Preserving Locality in Vision Transformers for Class Incremental Learning

https://github.com/bwnzheng/tagfex_cvpr2025

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Task-Agnostic Guided Feature Expansion for Class-Incremental Learning

https://github.com/canpeng123/fscil_alice

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Few-Shot Class-Incremental Learning from an Open-Set Perspective

https://github.com/chaoqi7/bsa-cil-3d

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Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training

https://github.com/chaoqi7/cmip-cil

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CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning

https://github.com/chen-xw/drc-cil

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Dynamic Residual Classifier for Class Incremental Learning

https://github.com/chen-yizhu/gacl

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GACL: Exemplar-Free Generalized Analytic Continual Learning

https://github.com/chrismcclurg/fscil-acs

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Active Class Selection for Few-Shot Class-Incremental Learning

https://github.com/clovaai/SSUL

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SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

https://github.com/conditionwang/fcil

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Federated Class-Incremental Learning

https://github.com/csm9493/TaskBalancedBN

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Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning

https://github.com/czjghost/afs

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New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

https://github.com/dawnliu35/fscil-asp

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Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt

https://github.com/demonjianz/s2c

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Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

https://github.com/dipamgoswami/fecam

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FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

https://github.com/dipamgoswami/fscil-calibration

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Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers

https://github.com/doubleclass/gmm

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Generative Multi-modal Models are Good Class-Incremental Learners

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