AINeutralarXiv – CS AI · Mar 34/105
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An Analysis of Multi-Task Architectures for the Hierarchic Multi-Label Problem of Vehicle Model and Make Classification
Researchers analyzed multi-task learning architectures for hierarchical classification of vehicle makes and models, testing CNN and Transformer models on StanfordCars and CompCars datasets. The study found that multi-task approaches improved performance for CNNs in almost all scenarios and yielded significant improvements for both model types on the CompCars dataset.