Deep networks have significantly improved the state of the arts for several tasks in computer vision. Unfortunately, the impressive performance gains have come at the price of the use of massive amounts of labeled data. As the cost of collecting and annotating data is often prohibitive, given a novel target task where few or no training samples are available, it would be desirable to build effective learners that can leverage information from labeled data of a different but related source domain. However, a major obstacle in adapting predictive models to the target task is the shift in data distributions across different domains. This problem, typically referred as domain shift, has motivated research into Domain Adaptation (DA).
- M. Mancini, H. Karaoguz, E. Ricci, P. Jensfelt, B. Caputo, Knowledge Is Never Enough: Towards Web Aided Deep Open World Recognition, ICRA 2019.
- M. Mancini, S.R. Bulò, B. Caputo, E. Ricci, AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs, CVPR 2019. (Spotlight Oral, 8% acceptance rate)
- S Roy, A Siarohin, E Sangineto, SR Bulo, N Sebe, E Ricci, Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss, CVPR 2019.
- M. Mancini, L. Porzi, S. Rota Bulò, B. Caputo, E. Ricci: Boosting Domain Adaptation by Discovering Latent Domains. CVPR 2018. (Spotlight Oral, 8% acceptance rate)