ODINDistributionDiscriminator
Description
The ODINDistributionDiscriminator
is a distribution discriminator that uses the
out-of-distribution detector for neural networks
(ODIN) method to compute distribution confidence scores. The object
contains a threshold that you can use to separate observations into in-distribution (ID) and
out-of-distribution (OOD) data sets.
Creation
Create an ODINDistributionDiscriminator
object using the networkDistributionDiscriminator
function and setting the method
input
argument to "odin"
.
Note
The object type depends on the method you specify when you use the networkDistributionDiscriminator
function. The method determines how the
software computes the distribution confidence scores. You can set the method
argument
to either "baseline"
, "odin"
,
"energy"
, or "hbos"
. For more information, see
Distribution Confidence Scores.
Properties
Object Functions
isInNetworkDistribution | Determine whether data is within the distribution of the network |
distributionScores | Distribution confidence scores |
Examples
More About
References
[5] Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu, “Generalized Out-of-Distribution Detection: A Survey” August 3, 2022, http://arxiv.org/abs/2110.11334.
[6] Lee, Kimin, Kibok Lee, Honglak Lee, and Jinwoo Shin. “A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks.” arXiv, October 27, 2018. http://arxiv.org/abs/1807.03888.