Title:
Solving the Flickering Problem in Modern Convolutional Neural Networks
Solving the Flickering Problem in Modern Convolutional Neural Networks
| dc.contributor.author | Sundaramoorthi, Ganesh | |
| dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
| dc.contributor.corporatename | United Technologies Research Center (UTRC) | en_US |
| dc.date.accessioned | 2020-02-21T15:40:16Z | |
| dc.date.available | 2020-02-21T15:40:16Z | |
| dc.date.issued | 2020-02-12 | |
| dc.description | Presented on February 12, 2020 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116. | en_US |
| dc.description | Ganesh Sundaramoorthi is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, conducting research in computer vision and machine learning, and building products in robotic inspection from this research. His fundamental optimization algorithms have led to advancements in motion-based video segmentation and detection. His group also developed technology for seismic image analysis, electron microscopy images, and medical (MRI & CT) images. | en_US |
| dc.description | Runtime: 49:16 minutes | en_US |
| dc.description.abstract | Deep Learning has revolutionized the AI field. Despite this, there is much progress needed to deploy deep learning in safety critical applications (such as autonomous aircraft). This is because current deep learning systems are not robust to real-world nuisances (e.g., viewpoint, illumination, partial occlusion). In this talk, we take a step in constructing robust deep learning systems by addressing the problem that state-of-the-art Convolution Neural Networks (CNN) classifiers and detectors are vulnerable to small perturbations, including shifts of the image or camera. While various forms of specially engineered “adversarial perturbations” that fool deep learning systems have been well documented, modern CNNs can surprisingly change classification up to 30% probability even for simple 1-pixel shifts of the image. This lack of translational stability seems to be partially the cause of “flickering” in state-of-the-art object detectors applied to video. In this talk, we introduce this phenomena, propose a solution, prove it analytically, validate it empirically, and explain why existing CNNs exhibit this phenomena. | en_US |
| dc.format.extent | 49:16 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/62464 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
| dc.subject | Convolution Neural Networks (CNN) | en_US |
| dc.subject | Flickering | en_US |
| dc.title | Solving the Flickering Problem in Modern Convolutional Neural Networks | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Machine Learning Center | |
| local.contributor.corporatename | College of Computing | |
| local.relation.ispartofseries | ML@GT Seminar Series | |
| relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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