Multi-Domain Learning

Do you want to know more about using unknown samples to tighten the classification boundaries? Check our paper “Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation”, just accepted for publication in the SIBGRAPI - Conference on Graphics, Patterns and Images.

In this paper, we addressed the multi-domain learning problem. We propose to prune a single model for multiple domains, making it more compact and efficient. To do so, we encourage multiple domains to share convolutional filters, enabling us to prune weights not used by any of the domains at test time.

Do you want to know more about pruning models for multiple domains according to a user budget? Check our paper “Budget-Aware Pruning for Multi-Domain Learning”, just accepted for publication in the International Conference on Image Analysis and Processing.