Perturb-and-Compare Approach for Detecting Out-of-Distribution Samples in Constrained Access Environments
Accessing machine learning models through remote APIs has been gaining prevalence following the recent trend of scaling up model parameters for increased performance. Even though these models exhibit remarkable ability, detecting out-of-distribution (OOD) samples remains a crucial safety concern for end users as these samples may induce unreliable outputs from the model. In this work, we propose an OOD detection framework, MixDiff, that is applicable even when the model's parameters or its activations are not accessible to the end user. To bypass the access restriction, MixDiff applies an identical input-level perturbation to a given target sample and a similar in-distribution (ID) sample, then compares the relative difference in the model outputs of these two samples. MixDiff is model-agnostic and compatible with existing output-based OOD detection methods. We provide theoretical analysis to illustrate MixDiff's effectiveness in discerning OOD samples that induce overconfident outputs from the model and empirically demonstrate that MixDiff consistently enhances the OOD detection performance on various datasets in vision and text domains.
Code (1)
Similar Papers 제목 키워드 기반
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning
Recent advances in deep learning have led to breakthroughs in the development of automated skin disease classification. As we observe an increasing interest in these models in the dermatology space, it is crucial to addr…
FairnessOut-of-Distribution DetectionOut of Distribution (OOD) DetectionTransfer LearningManifold-Aware Perturbations for Constrained Generative Modeling
Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modeling distributions where samples are constrained by equalities, as is frequ…
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Current machine learning models achieve super-human performance in many real-world applications. Still, they are susceptible against imperceptible adversarial perturbations. The most effective solution for this problem i…
Data AugmentationOut-of-Distribution DetectionDetecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score
Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions i…
Robust Out-of-distribution Detection for Neural Networks
Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in the real world. Existing approaches for detecting OOD examples work well when evaluated on benign in-distribution and OO…
Out-of-Distribution DetectionOut of Distribution (OOD) Detection