Learning-Deep-Learning

December 2019

tl;dr: A new benchmark measuring how well methods detect potentially hazardous anomalies in driving scenes.

Embeddings of intermediate layers hold important information for anomaly detection.

Bayesian DL: epistemic uncertainty, aleatoric uncertainty, distributional uncertainty
Novelty detection (Out of distribution detection): one class cls which aim at discriminative embeddings, density estimations, and generative reconstruction.
Softmax score is not a reliable score for anomaly detection
Most better performing methods require special loss that reduced segmentation accuracy (tradeoff between better outlier detection and error. Cf tradeoff between better uncertainty calib and error)
Learning anomaly detection from fixed OoD data is on par with unsupervised methods for most of the datasets. Void classifier is most practical way forward. A separate void class is concisely better than maximizing the softmax entropy. A separate void class is also most practical.

Lost & Found dataset is real dataset, it can be used to compare the performance on synthetic datasets to identify methods that detect image in-painting instead of anomalies.
Image inpainting postprocessing steps please refer to Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes IJCV 2018 (data augmentation with AR)
evaluation scores: AP, and FPR@0.95Recall

Foggy cityscape: dataset with adjustable visibilities.
WildDash ECCV 2018 and RobustVisionChallenge CVPR 2018 for semantic and instance segmentation
Safe Visual Navigation via Deep Learning and Novelty Detection (generative reconstruction)

The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation