PosCUDA: Position based Convolution for Unlearnable Audio Datasets. (arXiv:2401.02135v1 [cs.SD])

Deep learning models require large amounts of clean data to acheive good
performance. To avoid the cost of expensive data acquisition, researchers use
the abundant data available on the internet. This raises significant privacy
concerns on the potential misuse of personal data for model training without
authorisation. Recent works such as CUDA propose solutions to this problem by
adding class-wise blurs to make datasets unlearnable, i.e a model can never use
the acquired dataset for learning. However these methods often …

acquisition audio authorisation cost data datasets deep learning good internet large model training performance personal personal data privacy privacy concerns researchers training