Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones virtual assistants, labeling entrusted to the clients or labels are extracted in an automated way. Specifically, case of audio data, acquiring semantic annotations can be prohibitively expensive time-consuming. As result, abundance rema...

Our paper proposes a vision-based hand gesture recognition system with interactive training, aimed to achieve a user-independent application by on-line supervised training. Usual recognition systems involve a preliminary off-line training phase, separated from the recognition phase. If the system recognizes unknown (non-trainer) users the recognition rate of gesture classes could decrease. The ...

It is generally agreed that, for a given handwriting recognition task, a user dependent system usually outperforms a user independent system, as long as a sufficient amount of training data is available. When the amount of user training data is limited, however, such a performance gain is not guaranteed. One way to improve the performance is to make use of existing knowledge, contained in a ric...

Scalable training data generation is a critical problem in deep learning. We propose PennSyn2Real - photo-realistic synthetic dataset consisting of more than 100 000 4K images 20 types micro aerial vehicles (MAVs). The can be used to generate arbitrary numbers for high-level computer vision tasks such as MAV detection and classification. Our framework bootstraps chroma-keying, mature cinematogr...