Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
This addresses the need for resource-efficient OOD detection in AI deployments, though it appears incremental as it builds on existing mixup techniques.
The paper tackles the problem of out-of-distribution (OOD) detection for reliable AI model deployment by proposing Multiple Input Mixup (MIM), which improves OOD detection performance with only single-epoch fine-tuning and shows superior performance compared to state-of-the-art methods on CIFAR10 and CIFAR100 benchmarks.
Recent remarkable success in the deep-learning industries has unprecedentedly increased the need for reliable model deployment. For example, the model should alert the user if the produced model outputs might not be reliable. Previous studies have proposed various methods to solve the Out-of-Distribution (OOD) detection problem, however, they generally require a burden of resources. In this work, we propose a novel and simple method, Multiple Input Mixup (MIM). Our method can help improve the OOD detection performance with only single epoch fine-tuning. Our method does not require training the model from scratch and can be attached to the classifier simply. Despite its simplicity, our MIM shows competitive performance. Our method can be suitable for various environments because our method only utilizes the In-Distribution (ID) samples to generate the synthesized OOD data. With extensive experiments with CIFAR10 and CIFAR100 benchmarks that have been largely adopted in out-of-distribution detection fields, we have demonstrated our MIM shows comprehensively superior performance compared to the SOTA method. Especially, our method does not need additional computation on the feature vectors compared to the previous studies. All source codes are publicly available at https://github.com/ndb796/MultipleInputMixup.