{"627193":{"#nid":"627193","#data":{"type":"event","title":"PhD Proposal by Amirreza Shaban","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Meta-Learning Techniques for Few-Shot Object Recognition and Hyperparameter Tuning\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAmirreza Shaban\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Interactive Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECollege of Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Friday, October 11, 2019\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 12:00 pm \u0026ndash; 2:00 pm (EST)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E CODA C1215 \u0026nbsp;(12th floor)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Byron Boots (Advisor, \u0026nbsp;School of Interactive Computing, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. James Hays (School of Interactive Computing, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Dhruv Batra (School of Interactive Computing, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Fuxin Li (School of Electrical Engineering and Computer Science, Oregon State University)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDeep Neural Networks are powerful at solving classification problems in computer vision. However, learning classifiers with these models requires a large amount of labeled training data, and recent approaches have struggled to adapt to new classes in a data-efficient manner. On the other hand, the human brain is capable of utilizing already known knowledge in order to learn new concepts with fewer examples and less supervision. Many meta-learning algorithms have been proposed to fill this gap but they come with their practical and theoretical limitations. We review the well-known bi-level optimization as a general framework for few-shot learning and hyperparameter optimization and discuss the practical limitations of computing the full gradient. We provide theoretical guarantees for the convergence of the bi-level optimization using the approximated gradients computed by the truncated back-propagation. In the next step, we propose an empirical method for few-shot semantic segmentation: instead of solving the inner optimization, we propose to directly estimate its result by a general function approximator. Finally, we will discuss extensions of this work with the focus on learning to find objects when full supervision is not available for the few training examples.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Meta-Learning Techniques for Few-Shot Object Recognition and Hyperparameter Tuning"}],"uid":"27707","created_gmt":"2019-10-04 18:42:09","changed_gmt":"2019-10-04 18:42:09","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-10-11T13:00:00-04:00","event_time_end":"2019-10-11T15:00:00-04:00","event_time_end_last":"2019-10-11T15:00:00-04:00","gmt_time_start":"2019-10-11 17:00:00","gmt_time_end":"2019-10-11 19:00:00","gmt_time_end_last":"2019-10-11 19:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}