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We propose a tensor network that can learn to perform multiple tasks by adjusting the factors of each layer. Most of the existing methods for multi-task learning train a single network to extract task-specific features and subsequent prediction. We propose to use a single network with task-specific transformations that can extract task-specific features and perform task inference with small memory overhead. In particular, we transform features using low-rank updates in the convolution kernels. We present experiments on different datasets for multi-task and multi-domain learning and demonstrate that our method achieves state-of-the-art performance with minimal memory overhead compared to existing methods.
Yash Garg,Ashley Prater-Bennette, andM. Salman Asif
"Multi-task and multi-domain learning with tensor networks", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 125470W (14 June 2023); https://doi.org/10.1117/12.2663623
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Yash Garg, Ashley Prater-Bennette, M. Salman Asif, "Multi-task and multi-domain learning with tensor networks," Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 125470W (14 June 2023); https://doi.org/10.1117/12.2663623