Generalized ODIN: Detecting Out-of-distribution Image without Learning
from Out-of-distribution Data
- URL: http://arxiv.org/abs/2002.11297v2
- Date: Tue, 31 Mar 2020 18:13:34 GMT
- Title: Generalized ODIN: Detecting Out-of-distribution Image without Learning
from Out-of-distribution Data
- Authors: Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
- Abstract summary: We propose two strategies for freeing a neural network from tuning with OoD data, while improving its OoD detection performance.
We specifically propose to decompose confidence scoring as well as a modified input pre-processing method.
Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference.
- Score: 87.61504710345528
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep neural networks have attained remarkable performance when applied to
data that comes from the same distribution as that of the training set, but can
significantly degrade otherwise. Therefore, detecting whether an example is
out-of-distribution (OoD) is crucial to enable a system that can reject such
samples or alert users. Recent works have made significant progress on OoD
benchmarks consisting of small image datasets. However, many recent methods
based on neural networks rely on training or tuning with both in-distribution
and out-of-distribution data. The latter is generally hard to define a-priori,
and its selection can easily bias the learning. We base our work on a popular
method ODIN, proposing two strategies for freeing it from the needs of tuning
with OoD data, while improving its OoD detection performance. We specifically
propose to decompose confidence scoring as well as a modified input
pre-processing method. We show that both of these significantly help in
detection performance. Our further analysis on a larger scale image dataset
shows that the two types of distribution shifts, specifically semantic shift
and non-semantic shift, present a significant difference in the difficulty of
the problem, providing an analysis of when ODIN-like strategies do or do not
work.
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