Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metric

• URL: http://arxiv.org/abs/2502.16329v1
• Date: Sat, 22 Feb 2025 19:21:50 GMT
• Title: Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metric
• Authors: W. Max Schreyer, Christopher Anderson, Reid F. Thompson,
• Abstract summary: Failure of machine learning models to generalize to new data is a core problem limiting the reliability of AI systems.<n>We propose a standardized approach for assessing data similarity by constructing a supervised autoencoder for generalizability estimation (SAGE)<n>We show that out-of-the-box model performance increases after SAGE score filtering, even when applied to data from the model's own training and test datasets.
• Score: 0.09363323206192666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Failure of machine learning models to generalize to new data is a core problem limiting the reliability of AI systems, partly due to the lack of simple and robust methods for comparing new data to the original training dataset. We propose a standardized approach for assessing data similarity in a model-agnostic manner by constructing a supervised autoencoder for generalizability estimation (SAGE). We compare points in a low-dimensional embedded latent space, defining empirical probability measures for k-Nearest Neighbors (kNN) distance, reconstruction of inputs and task-based performance. As proof of concept for classification tasks, we use MNIST and CIFAR-10 to demonstrate how an ensemble output probability score can separate deformed images from a mixture of typical test examples, and how this SAGE score is robust to transformations of increasing severity. As further proof of concept, we extend this approach to a regression task using non-imaging data (UCI Abalone). In all cases, we show that out-of-the-box model performance increases after SAGE score filtering, even when applied to data from the model's own training and test datasets. Our out-of-distribution scoring method can be introduced during several steps of model construction and assessment, leading to future improvements in responsible deep learning implementation.

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