Thông tin tài liệu
Nhan đề : |
Meta-transfer learning for emotion recognition |
Tác giả : |
Nguyen, Dung Nguyen, Duc Thanh Sridha, Sridharan |
Năm xuất bản : |
2023 |
Nhà xuất bản : |
Springer |
Tóm tắt : |
Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient training data, pre-trained models are limited in their generalisation ability, leading to poor performance on novel test sets. To mitigate this challenge, transfer learning performed by fine-tuning pr-etrained models on novel domains has been applied. However, the fine-tuned knowledge may overwrite and/or discard important knowledge learnt in pre-trained models. In this paper, we address this issue by proposing a PathNet-based meta-transfer learning method that is able to (i) transfer emotional knowledge learnt from one visual/audio emotion domain to another domain and (ii) transfer emotional knowledge learnt from multiple audio emotion domains to one another to improve overall emotion recognition accuracy. |
Mô tả: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s00521-023-08248-y https://dlib.phenikaa-uni.edu.vn/handle/PNK/8280 |
Bộ sưu tập |
OER - Công nghệ thông tin |
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