Learning with Proxy Supervision for End-To-End Visual Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315382" target="_blank" >RIV/68407700:21230/17:00315382 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.semanticscholar.org/paper/Learning-with-proxy-supervision-for-end-to-end-vis-Cermak-Angelova/0a572c16e635312f118d1a53f0ff6446402d3c32" target="_blank" >https://www.semanticscholar.org/paper/Learning-with-proxy-supervision-for-end-to-end-vis-Cermak-Angelova/0a572c16e635312f118d1a53f0ff6446402d3c32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IVS.2017.7995690" target="_blank" >10.1109/IVS.2017.7995690</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning with Proxy Supervision for End-To-End Visual Learning

Popis výsledku v původním jazyce

Learning with deep neural networks forms the state-of-The-Art in many tasks such as image classification, image detection, speech recognition, text analysis. We here set out to gain understanding in learning in an 'end-To-end' manner for an autonomous vehicle, which refers to directly learning the decision which will result from the perception of the scene. For example, we consider learning a binary 'stop'/'go' decision, with respect to pedestrians, given the input image. In this work we propose to use additional information, referred to as 'proxy supervision', for improved learning and study its effects on the overall performance. We show that the proxy labels significantly improve the robustness of learning, while achieving as good, or better, accuracy than in the original task of binary classification.

Název v anglickém jazyce

Learning with Proxy Supervision for End-To-End Visual Learning

Popis výsledku anglicky

Learning with deep neural networks forms the state-of-The-Art in many tasks such as image classification, image detection, speech recognition, text analysis. We here set out to gain understanding in learning in an 'end-To-end' manner for an autonomous vehicle, which refers to directly learning the decision which will result from the perception of the scene. For example, we consider learning a binary 'stop'/'go' decision, with respect to pedestrians, given the input image. In this work we propose to use additional information, referred to as 'proxy supervision', for improved learning and study its effects on the overall performance. We show that the proxy labels significantly improve the robustness of learning, while achieving as good, or better, accuracy than in the original task of binary classification.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of IEEE Intelligent Vehicles Symposium

ISBN

978-1-5090-4804-5

ISSN

1931-0587

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

—

Místo konání akce

Redondo Beach

Datum konání akce

11. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000425212700001