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Discriminability Distillation in Group Representation Learning
- Source :
- Computer Vision – ECCV 2020 ISBN: 9783030586065, ECCV (10)
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
Abstract
- Learning group representation is a commonly concerned issue in tasks where the basic unit is a group, set, or sequence. Previously, the research community tries to tackle it by aggregating the elements in a group based on an indicator either defined by humans such as the quality and saliency, or generated by a black box such as the attention score. This article provides a more essential and explicable view. We claim the most significant indicator to show whether the group representation can be benefited from one of its element is not the quality or an inexplicable score, but the discriminability w.r.t. the model. We explicitly design the discrimiability using embedded class centroids on a proxy set. We show the discrimiability knowledge has good properties that can be distilled by a light-weight distillation network and can be generalized on the unseen target set. The whole procedure is denoted as discriminability distillation learning (DDL). The proposed DDL can be flexibly plugged into many group-based recognition tasks without influencing the original training procedures. Comprehensive experiments on various tasks have proven the effectiveness of DDL for both accuracy and efficiency. Moreover, it pushes forward the state-of-the-art results on these tasks by an impressive margin.
- Subjects :
- Sequence
business.industry
Computer science
media_common.quotation_subject
Class (philosophy)
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Group representation
010309 optics
Margin (machine learning)
Black box
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quality (business)
Artificial intelligence
Set (psychology)
Representation (mathematics)
business
computer
media_common
Subjects
Details
- ISBN :
- 978-3-030-58606-5
- ISBNs :
- 9783030586065
- Database :
- OpenAIRE
- Journal :
- Computer Vision – ECCV 2020 ISBN: 9783030586065, ECCV (10)
- Accession number :
- edsair.doi...........c5a12b7a11cc853cfc213cdac6d4a273
- Full Text :
- https://doi.org/10.1007/978-3-030-58607-2_1