TY - JOUR A1 - Schiller, Dominik A1 - Huber, Tobias A1 - Dietz, Michael A1 - André, Elisabeth T1 - Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition JF - Frontiers in Computer Science N2 - Deep learning approaches are now a popular choice in the field of automatic emotion recognition (AER) across various modalities. Due to the high costs of manually labeling human emotions however, the amount of available training data is relatively scarce in comparison to other tasks. To facilitate the learning process and reduce the necessary amount of training-data, modern approaches therefore often rely on leveraging knowledge from models that have already been trained on related tasks where data is available abundantly. In this work we introduce a novel approach to transfer learning, which addresses two shortcomings of traditional methods: The (partial) inheritance of the original models structure and the restriction to other neural network models as an input source. To this end we identify the parts in the input that have been relevant for the decision of the model we want to transfer knowledge from, and directly encode those relevant regions in the data on which we train our new model. To validate our approach we performed experiments on well-established datasets for the task of automatic facial expression recognition. The results of those experiments are suggesting that our approach helps to accelerate the learning process. UR - https://doi.org/10.3389/fcomp.2020.00006 Y1 - 2020 UR - https://doi.org/10.3389/fcomp.2020.00006 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57261 SN - 2624-9898 VL - 2 PB - Frontiers Media CY - Lausanne ER - TY - CHAP A1 - Schiller, Dominik A1 - Huber, Tobias A1 - Lingenfelser, Florian A1 - Dietz, Michael A1 - Seiderer, Andreas A1 - André, Elisabeth T1 - Relevance-based Feature Masking: Improving Neural Network based Whale Classification through Explainable Artificial Intelligence T2 - Proceedings Interspeech 2019 UR - https://doi.org/10.21437/Interspeech.2019-2707 Y1 - 2019 UR - https://doi.org/10.21437/Interspeech.2019-2707 SP - 2423 EP - 2427 PB - ISCA CY - Grenoble ER -