Aggregative Self-Supervised Feature Learning
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Self-supervised learning (SSL) is an efficient approach that addresses the issue of annotation shortage. The key part in SSL is its proxy task that defines the supervisory signals and drives the learning toward effective feature representations. However, most SSL approaches usually focus on a single proxy task, which greatly limits the expressive power of the learned features and therefore deteriorates the network generalization capacity. In this regard, we hereby propose three strategies of aggregation in terms of complementarity of various forms to boost the robustness of self-supervised learned features. In spatial context aggregative SSL, we contribute a heuristic SSL method that integrates two ad-hoc proxy tasks with spatial context complementarity, modeling global and local contextual features, respectively. We then propose a principled framework of multi-task aggregative self-supervised learning to form a unified representation, with an intent of exploiting feature complementarity among different tasks. Finally, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability. Our extensive experiments on 2D natural image and 3D medical image classification tasks under limited annotation scenarios confirm that the proposed aggregation strategies successfully boost the classification accuracy.
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