M.Sc Student | Sason Eliyahu |
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Subject | Structured Label Classification using Deep Learning |
Department | Department of Electrical Engineering | Supervisors | Professor Shie Mannor |
Professor Yacov Crammer | |
Full Thesis text | ![]() |
In this work, we present a hierarchical inter-class relationship model and train it using a newly proposed probability-based loss function, which we call soft-NLL. This loss function gives a probability weight according to the inter-class taxonomy graph distances. Our hierarchical model provides significantly better semantic generalization ability compared to a regular N-way classifier.
We further proposed an algorithm where given a probabilistic classification model it
can return the input corresponding super-group based on classes hierarchy without any further learning. We deploy it in two scenarios in which super-group retrieval can be useful. The first one, selective classification, deals with the problem of low-confidence classification, wherein a model is unable to make a successful exact classification. In this case, our algorithm returns a corresponding closest super-class prediction. In the second scenario, the proposed algorithm is used for the zero-shot learning problem. In this case, given a novel input, the algorithm returns its hierarchically related group, rather than generating a true unseen group. Extensive experiments with the two scenarios show that our proposed hierarchical model yields more accurate and meaningful super-class predictions compared to a regular N-way classifier because of its significantly better semantic generalization ability.