Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness

Fran Jelenić, Josip Jukić, Martin Tutek, Mate Puljiz, Jan Snajder

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Keywords: out-of-distribution detection, deep neural networks, transformers, representation analysis, uncertainty quantification, text classification

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TL;DR: BLOOD is a novel OOD detection method for deep neural networks, applicable to pre-trained models, relying on between-layer transformation smoothness, and outperforming similar methods.

Abstract: Effective out-of-distribution (OOD) detection is crucial for reliable machine learning models, yet most current methods are limited in practical use due to requirements like access to training data or intervention in training. We present a novel method for detecting OOD data in Transformers based on transformation smoothness between intermediate layers of a network (BLOOD), which is applicable to pre-trained models without access to training data. BLOOD utilizes the tendency of between-layer representation transformations of in-distribution (ID) data to be smoother than the corresponding transformations of OOD data, a property that we also demonstrate empirically. We evaluate BLOOD on several text classification tasks with Transformer networks and demonstrate that it outperforms methods with comparable resource requirements. Our analysis also suggests that when learning simpler tasks, OOD data transformations maintain their original sharpness, whereas sharpness increases with more complex tasks.

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Primary Area: general machine learning (i.e., none of the above)

Submission Number: 3628