Self-Supervised Features Improve Open-World Learning
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This is a position paper that addresses the problem of Open-World learning while proposing for the underlying feature representation to be learnt using self-supervision. We also present an unifying open-world framework combining three individual research dimensions which have been explored independently Incremental Learning, Out-of-Distribution detection and Open-World learning. We observe that the supervised feature representations are limited and degenerate for the Open-World setting and unsupervised feature representation is native to each of these three problem domains. Under an unsupervised feature representation, we categorize the problem of detecting unknowns as either Out-of-Label-space or Out-of-Distribution detection, depending on the data used during system training versus system testing. The incremental learning component of our pipeline is a zero-exemplar online model which performs comparatively against state-of-the-art on ImageNet-100 protocol and does not require any back-propagation or retraining of the underlying deep-network. It further outperforms the current state-of-the-art by simply using the same number of exemplars as its counterparts. To evaluate our approach for Open-World learning, we propose a new comprehensive protocol and evaluate its performance in both Out-of-Label and Out-of-Distribution settings for each incremental stage. We also demonstrate the adaptability of our approach by showing how it can work as a plug-in with any of the recently proposed self-supervised feature representation methods.
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