Zobrazeno 1 - 10
of 2 547
pro vyhledávání: '"ZHOU Da"'
Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. While recent advances in pre-trained models provide a solid foundation for DIL, learning new concepts often results in the cata
Externí odkaz:
http://arxiv.org/abs/2410.00911
In our ever-evolving world, new data exhibits a long-tailed distribution, such as e-commerce platform reviews. This necessitates continuous model learning imbalanced data without forgetting, addressing the challenge of long-tailed class-incremental l
Externí odkaz:
http://arxiv.org/abs/2409.07446
Autor:
Sun, Hai-Long, Zhou, Da-Wei, Li, Yang, Lu, Shiyin, Yi, Chao, Chen, Qing-Guo, Xu, Zhao, Luo, Weihua, Zhang, Kaifu, Zhan, De-Chuan, Ye, Han-Jia
The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V has marked a significant step towards artificial general intelligence. Existing methods mainly focus on aligning vision encoders with LLMs through supervised fine-tuning (S
Externí odkaz:
http://arxiv.org/abs/2406.02539
Quantifying the size of cell populations is crucial for understanding biological processes such as growth, injury repair, and disease progression. Often, experimental data offer information in the form of relative frequencies of distinct cell types,
Externí odkaz:
http://arxiv.org/abs/2405.04557
The era of pre-trained models has ushered in a wealth of new insights for the machine learning community. Among the myriad of questions that arise, one of paramount importance is: 'Do pre-trained models possess comprehensive knowledge?' This paper se
Externí odkaz:
http://arxiv.org/abs/2404.12407
Autor:
Sousa, Frederico B., Ames, Alessandra, Liu, Mingzu, Gastelois, Pedro L., Oliveira, Vinícius A., Zhou, Da, Matos, Matheus J. S., Chacham, Helio, Terrones, Mauricio, Teodoro, Marcio D., Malard, Leandro M.
Transition metal dichalcogenide (TMD) monolayers present a singular coupling in their spin and valley degrees of freedom. Moreover, by applying an external magnetic field it is possible to break the energy degeneracy between their K and $-$K valleys.
Externí odkaz:
http://arxiv.org/abs/2404.04131
Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Despite the strong performance of Pre-Trained Models (PTMs) in CIL, a critical issue persists: learning new classes often results in the
Externí odkaz:
http://arxiv.org/abs/2403.12030
Autor:
Zhou, Da, Pham, Yen Thi Hai, Dang, Diem Thi-Xuan, Sanchez, David, Oberoi, Aaryan, Wang, Ke, Fest, Andres, Sredenschek, Alexander, Liu, Mingzu, Terrones, Humberto, Das, Saptarshi, Le, Dai-Nam, Woods, Lilia M., Phan, Manh-Huong, Terrones, Mauricio
Monolayers of molybdenum disulfide (MoS2) are the most studied two-dimensional (2D) transition-metal dichalcogenides (TMDs), due to its exceptional optical, electronic, and opto-electronic properties. Recent studies have shown the possibility of inco
Externí odkaz:
http://arxiv.org/abs/2401.16806
Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve this goal and meanwhile overcome the catastrophic forgetting of former kn
Externí odkaz:
http://arxiv.org/abs/2401.16386
Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new classes and maintain the knowledge of base classes. In this Few-Shot Class-
Externí odkaz:
http://arxiv.org/abs/2312.05229