| Full text | |
| Author(s): |
Alvarenga e Silva, Lucas Fernando
;
dos Santos, Samuel Felipe
;
Sebe, Nicu
;
Almeida, Jurandy
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PATTERN RECOGNITION LETTERS; v. 189, p. 8-pg., 2025-02-28. |
| Abstract | |
Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA approaches in literature only align known classes or use supervised training to learn unknown classes as a single new category. In this work, we introduce anew approach to improve OSDA techniques by extracting a set of high-confidence unknown instances and using it as a hard constraint to tighten the classification boundaries. Specifically, we use anew loss constraint that is evaluated in three different ways: (1) using pristine negative instances directly; (2) using data augmentation techniques to create randomly transformed negatives; and (3) with generated synthetic negatives containing adversarial features. We analyze different strategies to improve the discriminator and the training of the Generative Adversarial Network (GAN) used to generate synthetic negatives. We conducted extensive experiments and analysis on OVANet using three widely-used public benchmarks, the Office-31, Office-Home, and VisDA datasets. We were able to achieve similar H-score to other state-of-the-art methods, while increasing the accuracy on unknown categories. (AU) | |
| FAPESP's process: | 21/13348-1 - Investigation of open set domain adaptation methods for computer vision tasks |
| Grantee: | Lucas Fernando Alvarenga e Silva |
| Support Opportunities: | Scholarships abroad - Research Internship - Master's degree |
| FAPESP's process: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 20/08770-3 - Open set methods based on deep networks for multimedia recognition |
| Grantee: | Lucas Fernando Alvarenga e Silva |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 24/04500-2 - Deep learning for computer vision: improving generalization for small amounts of data |
| Grantee: | Samuel Felipe dos Santos |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 23/17577-0 - Video understanding through deep learning with minimal human supervision |
| Grantee: | Jurandy Gomes de Almeida Junior |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/17874-0 - Multi-user Equipmente approved in grant 2013/08293-7, KAHUNA upgrade - HPE Apollo Gen10 supercomputer |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Multi-user Equipment Program |
| FAPESP's process: | 23/03328-9 - Machine Learning Under The Hood: Efficient Accelerators for Deep Networks and its Applicability in Scientific Computation |
| Grantee: | Lucas Fernando Alvarenga e Silva |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |