OODformer: Out-Of-Distribution Detection Transformer
This addresses a critical issue for safety-critical applications where models must identify OOD data to prevent performance degradation, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of out-of-distribution (OOD) detection in image classification by proposing OODformer, a transformer-based architecture that leverages global image context to discriminate between in-distribution and OOD samples, achieving state-of-the-art results on CIFAR-10/-100 and ImageNet30 datasets.
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.