Suchergebnisse - Continual Learning Online Learning Diffusion Model

Online Continual Learning of Diffusion Models: Multi-Mode Adaptive Generative Distillation

Autoren: Yang, Rui Grard, Matthieu Dellandréa, Emmanuel et al.

Weitere Verfasser: Yang, Rui Grard, Matthieu Dellandréa, Emmanuel et al.

Quelle: 2025 IEEE International Conference on Image Processing (ICIP). :1001-1006

Schlagwörter: Continual Learning, Continual Learning Online Learning Diffusion Model, Online Learning, Diffusion Model, [INFO] Computer Science [cs]

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04928776v2

Adaptive and Efficient Continual Learning in Dynamic Environments

Autoren: Yang, Rui yang, rui

Weitere Verfasser: Yang, Rui yang, rui

Schlagwörter: Continual Learning, Online Learning, Memory selection, Diffusion Model, Sélection de mémoire, Apprentissage Incrémental, [INFO] Computer Science [cs], Modèle de diffusion, Apprentissage continu

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/tel-04943017v2

Dealing with Synthetic Data Contamination in Online Continual Learning

Autoren: Wang, Maorong Michel, Nicolas Mao, Jiafeng et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2411.13852

Adaptive and Efficient Continual Learning in Dynamic Environments ; Apprentissage Continu Adaptatif et Efficace dans des Environnements Dynamiques

Autoren: Yang, Rui Extraction de Caractéristiques et Identification (imagine) Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) et al.

Weitere Verfasser: Yang, Rui Extraction de Caractéristiques et Identification (imagine) Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) et al.

Quelle: https://hal.science/tel-04943017 ; Computer Science [cs]. École Centrale de Lyon, 2025. English. ⟨NNT : ⟩.

Schlagwörter: Continual Learning, Online Learning, Memory selection, Diffusion Model, Modèle de diffusion, Apprentissage Incrémental, Apprentissage continu, Sélection de mémoire, [INFO]Computer Science [cs]

Verfügbarkeit: https://hal.science/tel-04943017
https://hal.science/tel-04943017v1/document
https://hal.science/tel-04943017v1/file/Thesis_continual_learning_YR.pdf

Exploring Kolmogorov–Arnold Network Expansions in Vision Transformers for Mitigation of Catastrophic Forgetting in Continual Learning.

Autoren: Ullah, Zahid Kim, Jihie

Quelle: Mathematics (2227-7390); Sep2025, Vol. 13 Issue 18, p2988, 29p

Schlagwörter: TRANSFORMER models, MULTILAYER perceptrons, MACHINE learning, LEARNING

Dynamic Expansion Diffusion Learning for Lifelong Generative Modelling

Autoren: Fei Ye Adrian G. Bors Kun Zhang

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 39:22101-22109

A central difference attention multi‐modal segmentation network integrating continual learning and graph convolution diffusion algorithms for complex road crack segmentation

Autoren: Gang Wang HuaJun Huang JunHui Wang et al.

Quelle: Computer-Aided Civil and Infrastructure Engineering.

A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks.

Autoren: Francies, Mariam Labib Khalil, Abeer Twakol Amer, Hanan M. et al.

Quelle: Complex & Intelligent Systems; Oct2025, Vol. 11 Issue 10, p1-28, 28p

ASTDT: an Interpretable Adaptive Spectro-Temporal Diffusion Transformer for audio deepfake detection.

Autoren: Wani, Taiba Maijd Qadri, Syed Asif Ahmad Ashraf, Arselan et al.

Quelle: EURASIP Journal on Information Security; 10/29/2025, Vol. 2025 Issue 1, p1-31, 31p

Schlagwörter: TRANSFORMER models, GENERALIZATION, FEATURE extraction, MACHINE learning, RESEARCH evaluation, SPEECH synthesis

The application of scaffolding instruction and AI-driven diffusion models in children's aesthetic education: A case study on teaching traditional chinese painting of the twenty-four solar terms in chinese culture.

Autoren: Liu, Ran Pang, Wei Chen, Junming et al.

Quelle: Education & Information Technologies; May2025, Vol. 30 Issue 7, p9129-9160, 32p

Schlagwörter: STUDENT engagement, TEACHING methods, AESTHETICS education, CHINESE painting, CULTURE diffusion

多模态持续学习方法研究进展. (Chinese)

Alternate Title: Research Progress on Multimodal Continual Learning Methods. (English)

Autoren: 张 伟 钱龙玥 张 林 et al.

Quelle: Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li; Sep2025, Vol. 40 Issue 5, p1122-1138, 17p

Schlagwörter: MACHINE learning, ARTIFICIAL intelligence

Analysis of Continual Learning Techniques for Image Generative Models with Learned Class Information Management.

Autoren: Togo, Taro Togo, Ren Maeda, Keisuke et al.

Quelle: Sensors (14248220); May2024, Vol. 24 Issue 10, p3087, 18p

Schlagwörter: INFORMATION resources management, MACHINE learning, AMNESIA

Multi-Task Diffusion Learning for Time Series Classification.

Autoren: Zheng, Shaoqiu Liu, Zhen Tian, Long et al.

Quelle: Electronics (2079-9292); Oct2024, Vol. 13 Issue 20, p4015, 19p

Schlagwörter: NATURAL language processing, COMPUTER vision, TIME series analysis, REINFORCEMENT learning, LEARNING modules, PARTICLE swarm optimization

AMGET: An Adaptive Multimodal Graph-Enhanced Transformer for Cybersecure Detection of Evolving Fake News on Social Media.

Autoren: Mohammed, Bahaa Kareem Nuiaa Alogaili, Riyadh Rahef Hasan, Baraa Mohammed et al.

Quelle: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 10, p166-183, 18p

Schlagwörter: SOCIAL media, MISINFORMATION, MULTIMODAL user interfaces, GRAPH neural networks, INTERNET security, DYNAMICAL systems, MACHINE learning

SCARF: Scalable Continual Learning Framework for Memory‐efficient Multiple Neural Radiance Fields.

Autoren: Wang, Yuze Wang, Junyi Wang, Chen et al.

Quelle: Computer Graphics Forum; Oct2024, Vol. 43 Issue 7, p1-12, 12p

Schlagwörter: IMPLICIT functions, COMPUTER vision, RADIANCE, MACHINE learning, DISTILLATION

Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG.

Autoren: Yang, Yufei Li, Mingai Liu, Jianhang

Quelle: Brain Sciences (2076-3425); Feb2025, Vol. 15 Issue 2, p98, 26p

Schlagwörter: GENERATIVE artificial intelligence, MACHINE learning, MOTOR imagery (Cognition), DATA augmentation, FEATURE extraction

Continual learning for surface defect segmentation by subnetwork creation and selection.

Autoren: Dekhovich, Aleksandr Bessa, Miguel A.

Quelle: Journal of Intelligent Manufacturing; Jun2025, Vol. 36 Issue 5, p3051-3065, 15p

Schlagwörter: FISHER discriminant analysis, MACHINE learning, SURFACE defects

Stability through plasticity: Finding robust memories through representational drift.

Autoren: Natrajan, Maanasa Fitzgerald, James E.

Quelle: Proceedings of the National Academy of Sciences of the United States of America; 11/11/2025, Vol. 122 Issue 45, p1-17, 29p

Schlagwörter: MEMORY, LEARNING, FAULT tolerance (Engineering), MENTAL representation, PHENOTYPIC plasticity, SYNAPSES, STABILITY (Mechanics)

Stimulus valence moderates self-learning.

Autoren: Jalalian, Parnian Svensson, Saga Golubickis, Marius et al.

Quelle: Cognition & Emotion; Sep2024, Vol. 38 Issue 6, p884-897, 14p

Schlagwörter: AUTODIDACTICISM, DRIFT diffusion models, REWARD (Psychology), REINFORCEMENT learning, STIMULUS & response (Psychology)

Deep Lidar-Guided Image Deblurring.

Autoren: Yi, Ziyao Valsesia, Diego Bianchi, Tiziano et al.

Quelle: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6786, 20p

Schlagwörter: LIDAR, IMAGE processing, IMAGE reconstruction, NEURAL computers, ARTIFICIAL neural networks, SMARTPHONES, MACHINE learning, DEPTH perception