Evaluating variational autoencoder methods for out-of-distribution detection in autonomous vehicles
In a safety-critical system like autonomous vehicles, it is essential to ensure that the observations shown are within the distribution of training data, otherwise they are called out-of-distribution (OOD). OOD detection is a fundamental problem that needs to be addressed to avoid errors in image re...
Saved in:
Main Author: | Dinh, Phuc Hung |
---|---|
Other Authors: | Arvind Easwaran |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/166524 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Disentangling latent space of variational autoencoder with distribution dependent guarantees for out-of-distribution detection and reasoning
by: Rahiminasab Zahra Reza (Zahra Rahiminasab)
Published: (2024) -
Out of distribution reasoning by weakly-supervised disentangled logic variational autoencoder
by: Rahiminasab, Zahra, et al.
Published: (2024) -
Fault detection and diagnosis in industrial processes with variational autoencoder: a comprehensive study
by: Zhu, Jinlin, et al.
Published: (2022) -
Evaluating & enhancing deep learning systems via out-of-distribution detection
by: Christopher, Berend David
Published: (2022) -
Towards out-of-distribution detection for object detection networks
by: Kanodia, Ritwik
Published: (2022)