arXiv Machine Learning

Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals

arXiv:2604. 02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored.

arXiv Machine Learning

Sep 25

AIR: Analytic Imbalance Rectifier for Continual Learning

The paper introduces AIR, an analytic imbalance rectifier designed for continual learning scenarios where data streams are evolving and imbalanced. AIR operates without exemplars, using a frozen backbone for feature extraction and a closed‑form incremental classifier that incorporates a class‑weighted ridge objective. It employs an analytic reweighting module to equalize sample weights across classes, achieving significant improvements in accuracy and macro F1 over 28 baseline methods in long‑tailed class‑incremental learning and over 15 baselines in the Si‑Blurry setting with recurring classes.

By Di Fang, Yinan Zhu, Zhiping Lin, Cen Chen, Ziqian Zeng, Huiping Zhuang

agents

arXiv Machine Learning

2d ago

Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

arXiv:2609.39839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuni...

fine-tuning benchmarks safety

arXiv Machine Learning

2d ago

Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning

The paper introduces TALON, a Task‑Adaptive LoRA‑Teacher framework for Few‑Shot Class‑Incremental Learning. TALON assigns a dedicated LoRA‑Teacher to each incremental task, then distills the frozen teachers into a single LoRA‑Student via Ensemble Knowledge Transfer, using a semantic‑guided weighting scheme to reduce forgetting and overfitting. Experiments on four FSCIL benchmarks show that TALON matches or surpasses state‑of‑the‑art accuracy while using up to 33× fewer deployment parameters and cutting inference time by 41.7%.

By Hongwei Zhao (School of Computer Science,Engineering, Beihang University), Rui Liu (School of Computer Science,Engineering, Beihang University), Yansong Liu (School of Computer Science,Engineering, Beihang University), Zhiyuan Zou (School of Computer Science,Engineering, Beihang University), Yong Chen (School of Computer Science, Beijing University of Posts,Telecommunications)

fine-tuning efficiency benchmarks

arXiv Computer Vision

Aug 26

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels

arXiv:2508.21424v3 Announce Type: replace Abstract: Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that...

By Lucas Rakotoarivony

computer-vision benchmarks

arXiv Machine Learning

Jun 5

Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning

arXiv:2606. 05675v1 Announce Type: new Abstract: Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge.

By Hongye Xu, Bartosz Krawczyk

rag benchmarks safety

arXiv Machine Learning

Sep 1

One Adapter, Many Tasks: Task-Conditioned Feature Transformations for Continual Learning

arXiv:2608.31096v1 Announce Type: cross Abstract: Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while...

By Yunxiang Fu, Meng Lou, Yizhou Yu

fine-tuning

arXiv Machine Learning

Jun 5

Revisiting Prototype Rehearsal for Exemplar-Free Continual Learning: Manifold-Aware Boundary Sampling with Adaptive Class-Balanced Loss

arXiv:2606. 05695v1 Announce Type: new Abstract: Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data.

By Hongye Xu, Bartosz Krawczyk

benchmarks

arXiv Machine Learning

Jul 7

DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised

arXiv:2311. 07461v3 Announce Type: replace Abstract: Autonomous systems (AS) often rely on Deep Neural Network (DNN) classifiers to operate in complex and dynamically changing environments.

By Abanoub Ghobrial, Kerstin Eder

agents fine-tuning

arXiv Computer Vision

Sep 16

HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning

arXiv:2604.15678v2 Announce Type: replace Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) met...

By Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee, JiHyun Kim, Soojin Jang, YoungBin Kim

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jun 11

RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning

arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.

By Atif Hassan, Swanand Khare, Jiaul H. Paik

fine-tuning efficiency benchmarks

Hugging Face Trending Papers

Jul 20

Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning

Class Incremental Learning (CIL) aims to learn new concepts consistently from a data stream without forgetting. Unlike typical CIL methods which need to learn a model from scratch, pre-trained model (PTM) can easily adapt to a new task with fine-tuning.

fine-tuning benchmarks

arXiv Machine Learning

Aug 5

When Classes Evolve: A Benchmark and Framework for Stage-Aware Class-Incremental Learning

arXiv:2602. 00573v2 Announce Type: replace Abstract: Class-Incremental Learning (CIL) aims to sequentially learn new classes while mitigating catastrophic forgetting of previously learned knowledge.

By Zheng Zhang, Tao Hu, Xueheng Li, Yang Wang, Rui Li, Jie Zhang, Chengjun Xie

benchmarks

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