MIXUP-BASED DEEP METRIC LEARNING APPROACHES FOR INCOMPLETE SUPERVISION

Author(s)

Buris, Luiz H. ; Pedronette, Daniel C. G. ; Papa, Joao P. ; Almeida, Jurandy ; Carneiro, Gustavo ; Faria, Fabio A. ; IEEE

• Document type: Journal article
• Source: 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP; v. N/A, p. 5-pg., 2022-01-01.

Abstract

Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real applications becomes challenging due to the large labeled collections required during training. Several works have pursued solutions to overcome it by proposing strategies that can learn more for less, e.g., weakly and semi-supervised learning approaches. As these approaches do not usually address memorization and sensitivity to adversarial examples, this paper presents three deep metric learning approaches combined with Mixup for incomplete-supervision scenarios. We show that some state-of-the-art approaches in metric learning might not work well in such scenarios. Moreover, the proposed approaches outperform most of them in different datasets. (AU)

• FAPESP's process: 18/23908-1 - Towards the Robustness in Deep Learning Architectures for e-Science Applications
• Grantee: Fabio Augusto Faria
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 21/01870-5 - Multi-level Representation Fusion Methods based on Weakly Supervised Learning
• Grantee: Luiz Henrique Buris
• Support Opportunities: Scholarships in Brazil - Master