Our module exhibits greater flexibility compared to other denoising models as it can be readily transferred to any 1D classifier, such as Transformer and CNN.
Compared to other forms of prior knowledge, such as cyclic frequency, classification labels are more readily obtainable without additional estimation, making them more suitable to guide BD in benefiting the downstream tasks.
In the context of multi-task learning, a key challenge lies in balancing different loss components. To address this problem, we employ the so-called uncertainty-aware weighing scheme.
Our framework seamlessly integrates the objective functions of BD and downstream classifier, thereby incorporating the classification labels as prior information into the BD optimization.
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