Type of Publication

Conference Papers

Date:

8 /

2020

Status

Preprint

DOI:

10.48550/arXiv.2005.14565

Probabilistic Object Classification using CNN ML-MAP layers

Featured in:

Workshop on Perception for Autonomous Driving (PAD), European Conference on Computer Vision (ECCV) 2020

Authors:

Gledson Melotti, Cristiano Premebida, Jordan Bird, Diego Faria and Nuno Gonçalves

Abstract

Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics. However, deep models often generate overconfident predictions precluding proper probabilistic interpretation which we argue is due to the nature of the SoftMax layer. To reduce the overconfidence without compromising the classification performance, we introduce a CNN probabilistic approach based on dis- tributions calculated in the network’s Logit layer. The approach enables Bayesian inference by means of ML and MAP layers. Experiments with calibrated and the proposed prediction layers are carried out on object classification using data from the KITTI database. Results are reported for camera (RGB) and LiDAR (range-view) modalities, where the new approach shows promising performance compared to SoftMax.

Citation
Gledson Melotti, Cristiano Premebida, Jordan Bird, Diego Faria and Nuno Gonçalves (2020). Probabilistic object classification using CNN ML-MAP layers. arXiv preprint arXiv:2005.14565.

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