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| Author(s): |
Gabriel Biscaro Cavallari
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2022-05-24 |
| Examining board members: |
Moacir Antonelli Ponti;
Zhao Liang;
Anderson de Rezende Rocha;
Eduardo Alves do Valle Junior
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| Advisor: | Moacir Antonelli Ponti |
| Abstract | |
Modern computer vision systems demonstrate outstanding performance on a variety of challenging benchmarks, such as object detection, image recognition and semantic image segmentation. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. However, massive amounts of manually labeled data is both expensive and impractical to scale. Therefore, learning without requiring manual annotation effort is of crucial importance in order to successfully take advantage of the vast amount of unlabeled visual data that is available today. To address this challenge, unsupervised and semi-supervised learning methods could be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. This research aims to investigate different architectures and training strategies that considers both unlabeled and limited labeled data. Our hypothesis is that this strategy improves the generalization and discrimination of the learned feature space. Through auxiliary tasks, different datasets and extensive experiments, we concluded that both semi-supervised and self-supervised learning followed by fine-tuning generate discriminative representations. Furthermore, these representations tend to be more robust to attacks when compared to those learned in purely supervised context (AU) | |
| FAPESP's process: | 19/02033-0 - A study of image representations from multiple domains using unsupervised and semi-supervised deep learning |
| Grantee: | Gabriel Biscaro Cavallari |
| Support Opportunities: | Scholarships in Brazil - Master |