Emotion Recognition from Speech, Text, and Facial Expressions Using Meta-Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2004-0_37
Reference14 articles.
1. Nguyen K, Sridharan S, Abbasnejad I, Dean D, Fookes C (2018) Meta transfer learning for facial emotion recognition. In: 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, pp 3543–3548
2. Aparna C (2022) Human emotion detection using convolutional neural networks with hyperparameter tuning
3. Wang W, Zhang J, Lin Z, Cui L, Zhang X (2023) Meta-learning improves emotion recognition. https://doi.org/10.1007/978-981-19-7184-6_2
4. Hang X, Wang Y, Zhang D (2023) Complex emotion recognition via facial expressions with label noises self-cure relation networks. Comput Intell Neuroscience 2023:1–10. https://doi.org/10.1155/2023/7850140
5. Nair LR, Maddula SNVS, Gupta R, Palaniswamy S (2021) Meta-learning: a new way to learn and comparison of machine learning versus meta-learning. In: Reddy VS, Prasad VK, Wang J, Reddy KTV (eds) Soft computing and signal processing. Advances in Intelligent Systems and Computing. vol 1325. Springer, Singapore. https://doi.org/10.1007/978-981-33-6912-228
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