OpenLDN: Learning to Discover Novel Classes for Open-World
Semi-Supervised Learning
- URL: http://arxiv.org/abs/2207.02261v1
- Date: Tue, 5 Jul 2022 18:51:05 GMT
- Title: OpenLDN: Learning to Discover Novel Classes for Open-World
Semi-Supervised Learning
- Authors: Mamshad Nayeem Rizve, Navid Kardan, Salman Khan, Fahad Shahbaz Khan,
Mubarak Shah
- Abstract summary: Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning.
Recent SSL methods can effectively leverage a large repository of unlabeled data to improve performance while relying on a small set of labeled data.
This work introduces OpenLDN that utilizes a pairwise similarity loss to discover novel classes.
- Score: 110.40285771431687
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Semi-supervised learning (SSL) is one of the dominant approaches to address
the annotation bottleneck of supervised learning. Recent SSL methods can
effectively leverage a large repository of unlabeled data to improve
performance while relying on a small set of labeled data. One common assumption
in most SSL methods is that the labeled and unlabeled data are from the same
underlying data distribution. However, this is hardly the case in many
real-world scenarios, which limits their applicability. In this work, instead,
we attempt to solve the recently proposed challenging open-world SSL problem
that does not make such an assumption. In the open-world SSL problem, the
objective is to recognize samples of known classes, and simultaneously detect
and cluster samples belonging to novel classes present in unlabeled data. This
work introduces OpenLDN that utilizes a pairwise similarity loss to discover
novel classes. Using a bi-level optimization rule this pairwise similarity loss
exploits the information available in the labeled set to implicitly cluster
novel class samples, while simultaneously recognizing samples from known
classes. After discovering novel classes, OpenLDN transforms the open-world SSL
problem into a standard SSL problem to achieve additional performance gains
using existing SSL methods. Our extensive experiments demonstrate that OpenLDN
outperforms the current state-of-the-art methods on multiple popular
classification benchmarks while providing a better accuracy/training time
trade-off.
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