论文标题
使用CNN ML-MAP层的概率对象分类
Probabilistic Object Classification using CNN ML-MAP layers
论文作者
论文摘要
当前，深网是自动驾驶和机器人技术中感觉知觉的最新信息。但是，深层模型通常会产生过度自信的预测，从而排除了适当的概率解释，我们认为这是由于软磁层的性质。为了减少过度自信而不损害分类性能，我们根据网络logit层中计算出的分布引入了CNN概率方法。该方法通过ML和地图层实现贝叶斯推断。使用KITTI数据库的数据对对象分类进行了校准和提出的预测层的实验。报告了相机（$ RGB $）和LIDAR（范围视图）方式的结果，与SoftMax相比，新方法显示出有希望的性能。
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics. However, deep models often generate overconfident predictions precluding proper probabilistic interpretation which we argue is due to the nature of the SoftMax layer. To reduce the overconfidence without compromising the classification performance, we introduce a CNN probabilistic approach based on distributions calculated in the network's Logit layer. The approach enables Bayesian inference by means of ML and MAP layers. Experiments with calibrated and the proposed prediction layers are carried out on object classification using data from the KITTI database. Results are reported for camera ($RGB$) and LiDAR (range-view) modalities, where the new approach shows promising performance compared to SoftMax.
