Deep Metric Learning with Adaptive Margin and Adaptive Scale for Acoustic Word Discrimination

10/26/2022
by   Myunghun Jung, et al.
0

Many recent loss functions in deep metric learning are expressed with logarithmic and exponential forms, and they involve margin and scale as essential hyper-parameters. Since each data class has an intrinsic characteristic, several previous works have tried to learn embedding space close to the real distribution by introducing adaptive margins. However, there was no work on adaptive scales at all. We argue that both margin and scale should be adaptively adjustable during the training. In this paper, we propose a method called Adaptive Margin and Scale (AdaMS), where hyper-parameters of margin and scale are replaced with learnable parameters of adaptive margins and adaptive scales for each class. Our method is evaluated on Wall Street Journal dataset, and we achieve outperforming results for word discrimination tasks.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/18/2015

Metric Learning with Adaptive Density Discrimination

Distance metric learning (DML) approaches learn a transformation to a re...
research
06/25/2020

Adaptive additive classification-based loss for deep metric learning

Recent works have shown that deep metric learning algorithms can benefit...
research
02/09/2018

Metric Learning via Maximizing the Lipschitz Margin Ratio

In this paper, we propose the Lipschitz margin ratio and a new metric le...
research
03/26/2020

Negative Margin Matters: Understanding Margin in Few-shot Classification

This paper introduces a negative margin loss to metric learning based fe...
research
10/07/2022

Learning to embed semantic similarity for joint image-text retrieval

We present a deep learning approach for learning the joint semantic embe...
research
03/22/2021

Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

This paper introduces a new fundamental characteristic, , the dynamic ra...
research
07/10/2023

Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis

Fueled by deep learning, computer-aided diagnosis achieves huge advances...

Please sign up or login with your details

Forgot password? Click here to reset