Learning Set Functions with Implicit Differentiation

Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. Ou et al. (2022) show this reduces to fixed point iterations; however, as the number of iterations increases, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address this challenge with implicit differentiation and examine the convergence conditions for the fixed-point iterations. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications including product recommendation, set anomaly detection and compound selection tasks.

A Segmentation-Oriented Inter-Class Transfer Method: Application to Retinal Vessel Segmentation

Retinal vessel segmentation, as a principal nonintrusive diagnose method for ophthalmology diseases or diabetics, suffers from data scarcity due to requiring pixel-wise labels. In this paper, we proposed a convenient patch-based two-stage transfer method. First, based on the information bottleneck theory, we insert one dimensionality-reduced layer for task-specific feature space. Next, the semi-supervised clustering is conducted to select instances, from different sources databases, possessing similarities in the feature space. Surprisingly, we empirically demonstrate that images from different classes possessing similarities contribute to better performance than some same-class instances. The proposed framework achieved an accuracy of 97%, 96.8%, and 96.77% on DRIVE, STARE, and HRF respectively, outperforming current methods and independent human observers (DRIVE (96.37%) and STARE (93.39%)).