Browsing by Author Sridha, Sridharan
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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 emo... |
