OODformer: Out-Of-Distribution Detection Transformer
- URL: http://arxiv.org/abs/2107.08976v1
- Date: Mon, 19 Jul 2021 15:46:38 GMT
- Title: OODformer: Out-Of-Distribution Detection Transformer
- Authors: Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan
G\"unnemann, Volker Tresp
- Abstract summary: In real-world safety-critical applications, it is important to be aware if a new data point is OOD.
This paper proposes a first-of-its-kind OOD detection architecture named OODformer.
- Score: 15.17006322500865
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A serious problem in image classification is that a trained model might
perform well for input data that originates from the same distribution as the
data available for model training, but performs much worse for
out-of-distribution (OOD) samples. In real-world safety-critical applications,
in particular, it is important to be aware if a new data point is OOD. To date,
OOD detection is typically addressed using either confidence scores,
auto-encoder based reconstruction, or by contrastive learning. However, the
global image context has not yet been explored to discriminate the non-local
objectness between in-distribution and OOD samples. This paper proposes a
first-of-its-kind OOD detection architecture named OODformer that leverages the
contextualization capabilities of the transformer. Incorporating the
trans\-former as the principal feature extractor allows us to exploit the
object concepts and their discriminate attributes along with their
co-occurrence via visual attention. Using the contextualised embedding, we
demonstrate OOD detection using both class-conditioned latent space similarity
and a network confidence score. Our approach shows improved generalizability
across various datasets. We have achieved a new state-of-the-art result on
CIFAR-10/-100 and ImageNet30.
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