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pro vyhledávání: '"Dong, Songlin"'
Existing prompt learning methods in Vision-Language Models (VLM) have effectively enhanced the transfer capability of VLM to downstream tasks, but they suffer from a significant decline in generalization due to severe overfitting. To address this iss
Externí odkaz:
http://arxiv.org/abs/2410.10247
The problem of Rehearsal-Free Continual Learning (RFCL) aims to continually learn new knowledge while preventing forgetting of the old knowledge, without storing any old samples and prototypes. The latest methods leverage large-scale pre-trained mode
Externí odkaz:
http://arxiv.org/abs/2407.10281
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode. Existing methods rely on a memory buffer composed of old samples to pr
Externí odkaz:
http://arxiv.org/abs/2404.13576
Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where
Externí odkaz:
http://arxiv.org/abs/2403.18201
In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay methods store a subset of the old images for joint training. In the sce
Externí odkaz:
http://arxiv.org/abs/2403.06670
Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forge
Externí odkaz:
http://arxiv.org/abs/2302.13334
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories. We initiate the study of data decentralized class
Externí odkaz:
http://arxiv.org/abs/2203.05984
Publikováno v:
In Pattern Recognition November 2024 155
The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN
Externí odkaz:
http://arxiv.org/abs/2004.10956
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