Papers with Code 아카이브

Incremental Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 22
보존된 평가 기록: 112

Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.

코드

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

https://github.com/alviur/cvpr_pfr

공식 코드

Continually Learning Self-Supervised Representations with Projected Functional Regularization

https://github.com/alviur/ldc

공식 코드

Exemplar-free Continual Representation Learning via Learnable Drift Compensation

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

공식 코드

Class-Incremental Learning with Strong Pre-trained Models

https://github.com/amrgomaaelhady/icregress

공식 코드

Looking for a better fit? An Incremental Learning Multimodal Object Referencing Framework adapting to Individual Drivers

https://github.com/anselmeamekoe/delayedlabelstream

공식 코드

Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection

https://github.com/anvoynov/RandomPathGAN

공식 코드

RPGAN: GANs Interpretability via Random Routing

https://github.com/anwarmaxsum/agla

공식 코드

Assessor-Guided Learning for Continual Environments

https://github.com/anwarmaxsum/pip

공식 코드

PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning

https://github.com/appledora/RECAST_ICLR25

공식 코드

RECAST: Reparameterized, Compact weight Adaptation for Sequential Tasks

https://github.com/arthurdouillard/dytox

공식 코드

DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

https://github.com/arthurdouillard/incremental_learning.pytorch

공식 코드

PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning

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

공식 코드

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

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

공식 코드

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

https://github.com/automan000/Camera-based-Person-ReID

공식 코드

Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization

https://github.com/aybora/Semi-Automatic-Video-Annotation-OGAM

공식 코드

Semi-Automatic Annotation For Visual Object Tracking

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

공식 코드

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

https://github.com/bigdata-inha/recall-oriented-cl-framework

공식 코드

Recall-Oriented Continual Learning with Generative Adversarial Meta-Model

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

공식 코드

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

https://github.com/blackblitz/bcl

공식 코드

On Sequential Loss Approximation for Continual Learning

https://github.com/bladedancer957/iner_rdp

공식 코드

Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition

https://github.com/bossdm/RCMDP

공식 코드

Robust Lagrangian and Adversarial Policy Gradient for Robust Constrained Markov Decision Processes

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

공식 코드

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

https://github.com/brcsomnath/fairl

공식 코드

Sustaining Fairness via Incremental Learning

https://github.com/brett-daley/dqn-lambda

공식 코드

Reconciling $λ$-Returns with Experience Replay

https://github.com/brjathu/RPSnet

공식 코드

An Adaptive Random Path Selection Approach for Incremental Learning

https://github.com/brjathu/iTAML

공식 코드

iTAML: An Incremental Task-Agnostic Meta-learning Approach

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

공식 코드

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

https://github.com/bwnzheng/lpa_icme2023

공식 코드

Preserving Locality in Vision Transformers for Class Incremental Learning

https://github.com/bwnzheng/tagfex_cvpr2025

공식 코드

Task-Agnostic Guided Feature Expansion for Class-Incremental Learning

https://github.com/byyx666/archcraft

공식 코드

Revisiting Neural Networks for Continual Learning: An Architectural Perspective

https://github.com/can-can-ya/QPMIL-VL

공식 코드

Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification

https://github.com/canpeng123/fscil_alice

공식 코드

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

https://github.com/ccc-frankfurt/DP-NAS

공식 코드

Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

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

공식 코드

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

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

공식 코드

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

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

공식 코드

Dynamic Residual Classifier for Class Incremental Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.