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This paper proposes a multi-modal cross learning approach to augment the neural network training phase by additional sensor data. The approach is multi-modal during training (i.e., radar Range-Doppler maps, thermal camera images, and RGB camera images are used for training). In inference, the approach is single-modal (i.e., only radar Range-Doppler maps are needed for classification). The proposed approach uses a multi-modal autoencoder training which creates a compressed data representation containing correlated features across modalities. The encoder part is then used as a pretrained network for the classification task. The benefits are that expensive sensors like high resolution thermal cameras are not needed in the application but a higher classification accuracy is achieved because of the multi-modal cross learning during training. The autoencoders can also be used to generate hallucinated data of the absent sensors. The hallucinated data can be used for user interfaces, a further classification, or other tasks. The proposed approach is verified within a simultaneous cooking process classification, 2x2 cooktop occupancy detection, and gesture recognition task. The main functionality is an overboil protection and gesture control of a 2x2 cooktop. The multi-modal cross learning approach considerably outperforms single-modal approaches on that challenging classification task.

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