Effective Subset Selection Through The Lens of Neural Network Pruning
Abstract: Having large amounts of annotated data significantly impacts the effectiveness of deep neural networks. However, the annotation task can be very expensive in some domains, such as medical data. Thus, it is important to select the data to be annotated wisely, which is known as the subset selection problem.
We investigate and establish a relationship between subset selection and neural network pruning, which is more widely studied.
Leveraging insights from network pruning, we propose utilizing the norm criterion of neural network features to improve subset selection methods. We empirically validate our proposed strategy on various networks and datasets, demonstrating enhanced accuracy.
This shows the potential of employing pruning tools for subset selection.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~bo_han2
Submission Number: 3596
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