Attention-based knowledge distillation in scene recognition: The impact of a DCT-driven loss
Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduce...
| Autores: | , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/715640 |
| Acesso em linha: | http://hdl.handle.net/10486/715640 https://dx.doi.org/10.1109/TCSVT.2023.3250031 |
| Access Level: | acceso abierto |
| Palavra-chave: | 2D frequency transform convolutional neural networks deep learning Knowledge distillation multi-attention scene recognition Telecomunicaciones |
| Resumo: | Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In this paper, we propose and analyze the use of a 2D frequency transform of the activation maps before transferring them. We pose that - by using global image cues rather than pixel estimates, this strategy enhances knowledge transferability in tasks such as scene recognition, defined by strong spatial and contextual relationships between multiple and varied concepts. To validate the proposed method, an extensive evaluation of the state of the art in scene recognition is presented. Experimental results provide strong evidence that the proposed strategy enables the student network to better focus on the relevant image areas learnt by the teacher network, hence leading to better descriptive features and higher transferred performance than every other state-of-the-art alternative |
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