CEU Electronic Theses and Dissertations, 2025
| Author | Zuroshvili, Elene |
|---|---|
| Title | Fashion Intelligence through Multitask Learning: A Unified Model for Product Classification and Retrieval |
| Summary | Online fashion platforms increasingly rely on automated systems to organize and retrieve prod- uct information. This thesis develops a multitask deep learning model that jointly performs category classification, attribute prediction, and image-based retrieval using the DeepFashion dataset. The model combines these tasks into a unified ResNet-50-based architecture with separate output heads, trained using cross-entropy and triplet loss. Five configurations are compared: single-task classifiers for category and attributes, a standalone retrieval model, a dual-head classification model, and a full multitask model incorporating all three objectives. While multitask learning introduces challenges in balancing competing losses, a staged training pipeline improves stability and leads to performance comparable to single-task baselines. Be- yond model performance, the thesis discusses how such systems can help reduce search frictions and improve product discoverability in online fashion markets. |
| Supervisor | Posfai, Marton |
| Department | Undergraduate Studies BA |
| Full text | https://www.etd.ceu.edu/2025/zuroshvili_elene.pdf |
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