# Probabilistic Object Classification using CNN ML-MAP layers

arXiv: https://arxiv.org/abs/2005.14565 · HTML: https://mlsanity.com/paper/2005.14565/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 3 of 3)

- [Identifying Spurious Correlations and Correcting them with an Explanation-based Learning](https://mlsanity.com/paper/2211.08285/) (2022)
- [Reducing Overconfidence Predictions for Autonomous Driving Perception](https://mlsanity.com/paper/2202.07825/) (2022)
- [Probabilistic Approach for Road-Users Detection](https://mlsanity.com/paper/2112.01360/) (2021)

## Abstract (arXiv)

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 distributions 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.