EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference

• URL: http://arxiv.org/abs/2106.13842v2
• Date: Tue, 29 Jun 2021 01:27:49 GMT
• Title: EARLIN: Early Out-of-Distribution Detection for Resource-efficient Collaborative Inference
• Authors: Sumaiya Tabassum Nimi, Md Adnan Arefeen, Md Yusuf Sarwar Uddin, Yugyung Lee
• Abstract summary: Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs to a server. While this setup works cost-effectively for successful inferences, it severely underperforms when the model faces input samples on which the model was not trained. We propose a novel lightweight OOD detection approach that mines important features from the shallow layers of a pretrained CNN model.
• Score: 4.826988182025783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e.g., images) to a server (i.e., cloud) where the heavy deep learning models run. While this setup works cost-effectively for successful inferences, it severely underperforms when the model faces input samples on which the model was not trained (known as Out-of-Distribution (OOD) samples). If the edge devices could, at least, detect that an input sample is an OOD, that could potentially save communication and computation resources by not uploading those inputs to the server for inference workload. In this paper, we propose a novel lightweight OOD detection approach that mines important features from the shallow layers of a pretrained CNN model and detects an input sample as ID (In-Distribution) or OOD based on a distance function defined on the reduced feature space. Our technique (a) works on pretrained models without any retraining of those models, and (b) does not expose itself to any OOD dataset (all detection parameters are obtained from the ID training dataset). To this end, we develop EARLIN (EARLy OOD detection for Collaborative INference) that takes a pretrained model and partitions the model at the OOD detection layer and deploys the considerably small OOD part on an edge device and the rest on the cloud. By experimenting using real datasets and a prototype implementation, we show that our technique achieves better results than other approaches in terms of overall accuracy and cost when tested against popular OOD datasets on top of popular deep learning models pretrained on benchmark datasets.

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