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pro vyhledávání: '"Zhang, Shuoxi"'
Humans exhibit remarkable proficiency in visual classification tasks, accurately recognizing and classifying new images with minimal examples. This ability is attributed to their capacity to focus on details and identify common features between previ
Externí odkaz:
http://arxiv.org/abs/2408.01427
The advent of Vision Transformers (ViTs) marks a substantial paradigm shift in the realm of computer vision. ViTs capture the global information of images through self-attention modules, which perform dot product computations among patchified image t
Externí odkaz:
http://arxiv.org/abs/2406.00427
Knowledge distillation is a powerful technique for transferring knowledge from a pre-trained teacher model to a student model. However, the true potential of knowledge transfer has not been fully explored. Existing approaches primarily focus on disti
Externí odkaz:
http://arxiv.org/abs/2306.12442
Knowledge distillation aims to transfer knowledge to the student model by utilizing the predictions/features of the teacher model, and feature-based distillation has recently shown its superiority over logit-based distillation. However, due to the cu
Externí odkaz:
http://arxiv.org/abs/2211.14773
Model-Agnostic Meta-Learning (MAML) is a famous few-shot learning method that has inspired many follow-up efforts, such as ANIL and BOIL. However, as an inductive method, MAML is unable to fully utilize the information of query set, limiting its pote
Externí odkaz:
http://arxiv.org/abs/2207.04217
Publikováno v:
In Big Data Research 28 May 2024 36
Autor:
Liao, Yongkai, Zhang, Shuoxi
Publikováno v:
In Journal of Mathematical Analysis and Applications 15 May 2018 461(2):1009-1052