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