Incremental Learning
386 papers with code • 22 benchmarks • 9 datasets
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.
Benchmarks
These leaderboards are used to track progress in Incremental Learning
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Libraries
Use these libraries to find Incremental Learning models and implementationsDatasets
CIFAR-100
Permuted MNIST
CORe50
INRIA Aerial Image Labeling
TopLogo-10
Concept-1K
HOWS
Latest papers
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
Revisiting Neural Networks for Continual Learning: An Architectural Perspective
This paper seeks to bridge this gap between network architecture design and CL, and to present a holistic study on the impact of network architectures on CL.
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
dipamgoswami/fscil-calibration
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FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards.
DELTA: Decoupling Long-Tailed Online Continual Learning
A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge.
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners.
Generative Multi-modal Models are Good Class-Incremental Learners
doubleclass/gmm
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In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge.
OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
noorahmedds/orco
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To address these challenges, we propose the OrCo framework built on two core principles: features' orthogonality in the representation space, and contrastive learning.
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
ZHUANGHP/Analytic-continual-learning
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The compensation stream is governed by a Dual-Activation Compensation (DAC) module.
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments.
G-ACIL: Analytic Learning for Exemplar-Free Generalized Class Incremental Learning
ZHUANGHP/Analytic-continual-learning
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The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution, leading to intensified forgetting.
