# Early-exit deep neural networks for distorted images: providing an efficient edge offloading

arXiv: https://arxiv.org/abs/2108.09343 · HTML: https://mlsanity.com/paper/2108.09343/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Placed in

- Efficient LLM inference and serving / [Early exit and edge inference](https://mlsanity.com/inference/early-exit-and-edge-inference/)

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud](https://mlsanity.com/paper/2008.06402/) (2020)
- [On Classification of Distorted Images with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.01924/) (2017)
- [DeepCorrect: Correcting DNN models against Image Distortions](https://mlsanity.com/paper/1705.02406/) (2017)
- [Towards Robust Deep Neural Networks with BANG](https://mlsanity.com/paper/1612.00138/) (2016)
- [Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks](https://mlsanity.com/paper/2010.16335/) (2020)
- [Inference Time Optimization Using BranchyNet Partitioning](https://mlsanity.com/paper/2005.04099/) (2020)

## Built on by (most cited first, 8 of 8)

- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [SplitEE: Early Exit in Deep Neural Networks with Split Computing](https://mlsanity.com/paper/2309.09195/) (2023)
- [Unsupervised Early Exit in DNNs with Multiple Exits](https://mlsanity.com/paper/2209.09480/) (2022)
- [I-SplitEE: Image classification in Split Computing DNNs with Early Exits](https://mlsanity.com/paper/2401.10541/) (2024)
- [Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach](https://mlsanity.com/paper/2410.05338/) (2024)
- [Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach](https://mlsanity.com/paper/2412.16616/) (2024)
- [Know What You Don't Know: Selective Prediction for Early Exit DNNs](https://mlsanity.com/paper/2509.11520/) (2025)
- [A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks](https://mlsanity.com/paper/2604.24810/) (2026)

## Abstract (arXiv)

Edge offloading for deep neural networks (DNNs) can be adaptive to the input's complexity by using early-exit DNNs. These DNNs have side branches throughout their architecture, allowing the inference to end earlier in the edge. The branches estimate the accuracy for a given input. If this estimated accuracy reaches a threshold, the inference ends on the edge. Otherwise, the edge offloads the inference to the cloud to process the remaining DNN layers. However, DNNs for image classification deals with distorted images, which negatively impact the branches' estimated accuracy. Consequently, the edge offloads more inferences to the cloud. This work introduces expert side branches trained on a particular distortion type to improve robustness against image distortion. The edge detects the distortion type and selects appropriate expert branches to perform the inference. This approach increases the estimated accuracy on the edge, improving the offloading decisions. We validate our proposal in a realistic scenario, in which the edge offloads DNN inference to Amazon EC2 instances.