# Progressive Neural Networks for Transfer Learning in Emotion Recognition

arXiv: https://arxiv.org/abs/1706.03256 · HTML: https://mlsanity.com/paper/1706.03256/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech](https://mlsanity.com/paper/2201.07876/) (2022)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation](https://mlsanity.com/paper/2109.02915/) (2021)
- [Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects](https://mlsanity.com/paper/2106.14174/) (2021)
- [CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer](https://mlsanity.com/paper/2206.08883/) (2022)
- [CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition](https://mlsanity.com/paper/2208.07221/) (2022)
- [Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine](https://mlsanity.com/paper/2105.09406/) (2021)
- [EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning](https://mlsanity.com/paper/2205.10356/) (2022)
- [Transfer learning strategies for accelerating reinforcement-learning-based flow control](https://mlsanity.com/paper/2510.16016/) (2025)
- [MF-GCN: A Multi-Frequency Graph Convolutional Network for Tri-Modal Depression Detection Using Eye-Tracking, Facial, and Acoustic Features](https://mlsanity.com/paper/2511.15675/) (2025)

## Abstract (arXiv)

Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another. In this paper, we investigate how knowledge can be transferred between three paralinguistic tasks: speaker, emotion, and gender recognition. Further, we extend this problem to cross-dataset tasks, asking how knowledge captured in one emotion dataset can be transferred to another. We focus on progressive neural networks and compare these networks to the conventional deep learning method of pre-training and fine-tuning. Progressive neural networks provide a way to transfer knowledge and avoid the forgetting effect present when pre-training neural networks on different tasks. Our experiments demonstrate that: (1) emotion recognition can benefit from using representations originally learned for different paralinguistic tasks and (2) transfer learning can effectively leverage additional datasets to improve the performance of emotion recognition systems.