Log In Sign Up

Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation

by   Marvin Klingner, et al.

While current approaches for neural network training often aim at improving performance, less focus is put on training methods aiming at robustness towards varying noise conditions or directed attacks by adversarial examples. In this paper, we propose to improve robustness by a multi-task training, which extends supervised semantic segmentation by a self-supervised monocular depth estimation on unlabeled videos. This additional task is only performed during training to improve the semantic segmentation model's robustness at test time under several input perturbations. Moreover, we even find that our joint training approach also improves the performance of the model on the original (supervised) semantic segmentation task. Our evaluation exhibits a particular novelty in that it allows to mutually compare the effect of input noises and adversarial attacks on the robustness of the semantic segmentation. We show the effectiveness of our method on the Cityscapes dataset, where our multi-task training approach consistently outperforms the single-task semantic segmentation baseline in terms of both robustness vs. noise and in terms of adversarial attacks, without the need for depth labels in training.


page 1

page 3


Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation

For a robot deployed in the world, it is desirable to have the ability o...

Composite Learning for Robust and Effective Dense Predictions

Multi-task learning promises better model generalization on a target tas...

Improving Online Performance Prediction for Semantic Segmentation

In this work we address the task of observing the performance of a seman...

On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption

Semantic segmentation is a scene understanding task at the heart of safe...

Detecting Adversarial Perturbations in Multi-Task Perception

While deep neural networks (DNNs) achieve impressive performance on envi...

Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness

In this paper, we present a strategy for training convolutional neural n...

SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness

In this paper, we present a strategy for training convolutional neural n...