arXiv Machine Learning

Breaking the Synthetic-Real Domain Shortcut for Training-Free Generative Replay-based Class Incremental Learning

arXiv:2607. 22994v1 Announce Type: cross Abstract: Class-incremental learning (CIL) requires models to continuously acquire new knowledge while avoiding catastrophic forgetting.

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

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

Jul 31

Continual Learning with Vision-Language Models via Semantic-Geometry Preservation

arXiv:2603. 12055v3 Announce Type: replace-cross Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion.

By Chiyuan He, Zihuan Qiu, Fanman Meng, Runtong Zhang, Linfeng Xu, Qingbo Wu, Hongliang Li

llms efficiency multimodal benchmarks safety

arXiv AI

Jun 30

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.

By Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer, Mukesh Prasad, Weiping Ding, Yew-Soon Ong

llms diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 31

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.

By Alexi Gladstone, Heng Ji, Yilun Du

diffusion benchmarks

arXiv Machine Learning

Jun 30

Prototype Latent World Model Replay for Class-Incremental Learning

arXiv:2606. 29465v1 Announce Type: new Abstract: Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones.

By Weizhi Nie, Hui Wang, Weijie Wang, Yuting Su

fine-tuning benchmarks

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

Jul 31

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

arXiv:2508. 04227v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot generalization.

By Yuyang Liu, Qiuhe Hong, Linlan Huang, Alexandra Gomez-Villa, Dipam Goswami, Tiantian Peng, Xialei Liu, Joost van de Weijer, Yonghong Tian

llms agents robotics multimodal benchmarks safety

arXiv AI

3d ago

Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers

arXiv:2509.23235v3 Announce Type: replace-cross Abstract: Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterat...

By Seongsoo Heo, Dong-Wan Choi

llms efficiency benchmarks

arXiv Machine Learning

Jun 25

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

arXiv:2511. 11421v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.

By Lan Li, Tao Hu, Da-Wei Zhou, Jia-Qi Yang, Han-Jia Ye, De-Chuan Zhan

llms multimodal benchmarks

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

Aug 31

Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay

The paper presents a generative continual learning framework that extends self‑organizing maps (SOMs) with learned distributional statistics and encoder–decoder models for class‑incremental learning. By storing running means, variances, and covariances for each SOM unit, the method can generate synthetic samples for replay without storing raw data, enabling exemplar‑free learning. Experiments on CIFAR‑10, CIFAR‑100, and TinyImageNet show competitive or superior performance compared to state‑of‑the‑art memory‑based and memory‑free methods, and the approach also allows easy visualization and post‑training generative use.

By Pujan Thapa, Alexander Ororbia, Travis Desell

diffusion benchmarks

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