Web: http://arxiv.org/abs/2207.13916

Sept. 23, 2022, 1:15 a.m. | Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash, Harshad Khadilkar, Chetan Arora

cs.CV updates on arXiv.org arxiv.org

Modern deep neural network models are known to erroneously classify
out-of-distribution (OOD) test data into one of the in-distribution (ID)
training classes with high confidence. This can have disastrous consequences
for safety-critical applications. A popular mitigation strategy is to train a
separate classifier that can detect such OOD samples at the test time. In most
practical settings OOD examples are not known at the train time, and hence a
key question is: how to augment the ID data with synthetic …

arxiv augmentation data detection distribution

More from arxiv.org / cs.CV updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France