Can RMD's performance gains on near-OOD detection for vision models be replicated for multimodal models (e.g.,

Abstract

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to

Research Question

Can RMD's performance gains on near-OOD detection for vision models be replicated for multimodal models (e.g., CLIP) when evaluated on ImageNet-30 versus CIFAR-100 distribution shifts, measured by AUROC and FPR95?

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