Pixel Club: Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning

דובר:
עמית שקד (אונ' תל-אביב)
תאריך:
יום שלישי, 20.6.2017, 11:30
מקום:
חדר 1061, בניין מאייר, הפקולטה להנדסת חשמל

I'll present two new concepts in deep learning and show how we used them to achieve a significant improvement in stereo matching, which is one of the most fundamental problems in computer vision. The first is a new residual architecture dedicated for metric learning, and the second is a general way to assess the confidence in the network's prediction.

*Amit is a deep learning and computer vision engineer at Magic Leap and M.Sc student in deep learning and computer vision at CS-TAU

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