Conference paper Open Access

INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELS

Serra, Joan; Álvarez, David; Gómez, Vicenç; Slizovskaia , Olga; F. Núñez, José; Luque, Jordi

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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.5253740",
"title": "INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELS",
"issued": {
"date-parts": [
[
2020,
4,
30
]
]
},
"abstract": "<p>Likelihood-based generative models are a promising resource to detect out-of- distribution (OOD) inputs which could compromise the&nbsp;robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that sig- nificantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models&rsquo; likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model compari- son. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.</p>",
"author": [
{
"family": "Serra, Joan"
},
{
"family": "\u00c1lvarez, David"
},
{
"family": "G\u00f3mez, Vicen\u00e7"
},
{
"family": "Slizovskaia , Olga"
},
{
"family": "F. N\u00fa\u00f1ez, Jos\u00e9"
},
{
"family": "Luque, Jordi"
}
],
"id": "5253740",
"note": "Accepted for ICLR2020",
"type": "paper-conference",
"event": "Ninth International Conference on Learning Representation (ICLR 2020)"
}
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