The Princeton AI Club is excited to announce its next guest speaker – Dr. Naila Murray from Facebook AI Research!
Her talk will focus on how to train deep vision models in low-data regimes
If you want to hear all about how to train your network with little data, sign-up for her virtual talk here, and join us on August 1st, at 11 am Eastern Time (ET)!
The full details are below.
Speaker: Dr. Naila Murray (Facebook AI Research)
Title: Training deep vision models in low-data regimes
Date: Monday, August 1st 2022, 11:00 AM ET
Abstract: Large-scale vision models, trained on vast quantities of often unlabelled data, give state-of-the-art performance when fine-tuned for a wide variety of downstream tasks. For these downstream tasks, the amount of training data available is usually orders of magnitude less than what was used during pre-training. And in many cases, there is little to no labelled data available. In this talk, I discuss approaches to fine-tuning pre-trained models for vision tasks in low-data regimes. In all cases, which cover retrieval, video segmentation, and object detection, I show that incorporating domain-specific and modality-specific inductive biases lead to improved model performance when training data is limited.
Bio:
Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in
2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval and visual attention. From 2015 to 2019 she led the computer vision team at Xerox Research Centre Europe, and continued
to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Facebook AI Research where she is a senior research engineering manager for EMEA.
She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and program chair for ICLR 2021. Her current research interests include few-shot learning and domain adaptation.