Weakly-Supervised Multi-Task Learning for Audio-Visual Speaker Verification

In this paper, we present a methodology for achieving robust multimodal person representations optimized for open-set audio-visual speaker verification. Distance Metric Learning (DML) approaches have typically dominated this problem space, owing to strong performance on new and unseen classes. In our work, we explored multitask learning techniques to further boost performance of the DML approach and show that an auxiliary task with weak labels can increase the compactness of the learned speaker representation. We also extend the Generalized end-to-end loss (GE2E) to multimodal inputs and demonstrate that it can achieve competitive performance in an audio-visual space. Finally, we introduce a non-synchronous audio-visual sampling random strategy during training time that has shown to improve generalization. Our network achieves state of the art performance for speaker verification, reporting 0.244%, 0.252%, 0.441% Equal Error Rate (EER) on the three official trial lists of VoxCeleb1-O/E/H, which is to our knowledge, the best published results on VoxCeleb1-E and VoxCeleb1-H

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets

This is the pre-review, version of the paper published in Medical Physics, Jan 2019Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is challenging, and recent work has demonstrated that convolutional neural networks perform well for this task. In this study, we have investigated the performance of several 2D U-Net and 3D U-Net configurations using both fat-suppressed and nonfat suppressed images. We have also assessed the effect of changing the number and quality of the ground truth segmentations.This research received support from the Sunnybrook Research Institute, through funding from the Federal Economic Development Agency for Southern Ontario (FedDev Ontario). This work was also funded in part by the Canadian Breast Cancer Foundation