This is a spotlight presentation material for the paper of "Symmetrical Synthesis for Deep Metric Learning" accepted in AAAI 2020. Written by Geonmo Gu*, Byungsoo Ko* (* Authors contributed equally.) @NAVER/LINE Vision - Arxiv: https://arxiv.org/abs/2001.11658 - Github: https://github.com/clovaai/symmetrical-synthesis

1. 1. Given two positive points and negative points in an
embedding space, the negative points generate their
synthetic points with each other as an axis of symmetry.
2. It selects the hardest negative point within the four
feature points: two original points and two synthetic
points.
Introduction
• We propose a novel method for synthetic hard sample
generation: Symmetrical Synthesis (Symm).
• In contrast to previous methods, it only requires simple
algebraic computation to generate synthetic hard samples.
• Hyper-parameter free, plug-and-play, no network modification,
no influence to training speed and optimization difficulty.
• Our method outperforms over existing methods for a variety
of losses on clustering and image retrieval tasks.
Motivation
Contribution
• Hard sample generation methods have been proposed to
improve metric learning losses (triplet, N-pair, lifted
structure, angular).
• Previous methods (DAML, HDML) uses generative networks,
which leads to more hyper-parameters, harder optimization,
slower training speed.
Symmetrical Synthesis
Symmetrical Synthesis for Deep Metric Learning
Geonmo Gu*, Byungsoo Ko*
Clova Vision, NAVER Corp. * Equal contribution
negative original point
Class A
Class B
negative synthetic point
positive original point

2. A t-SNE visualization of Symm + N-pair loss with the original (blue) and symmetrical
synthetic (red) feature points from the training set of CARS196 dataset.
Effect of Symmetrical Synthesis
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Paper number: VIS3219
Paper Link
Symmetrical Synthesis for Deep Metric Learning
Geonmo Gu*, Byungsoo Ko*
Clova Vision, NAVER Corp. * Equal contribution