# Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision

Source: [Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision](<https://www.sota2.com/research/paper/68fa64e41e209131cea072c5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:13:58.604Z

Authors: Yue Bai, Shuvra S. Bhattacharyya, Antti P. Happonen, Heikki Huttunen

Published: 2018-07-02T03:51:53.000Z

## Abstract

We propose a new framework for image classification with deep neural networks. The framework introduces intermediate outputs to the computational graph of a network. This enables flexible control of the computational load and balances the tradeoff between accuracy and execution time.   Moreover, we present an interesting finding that the intermediate outputs can act as a regularizer at training time, improving the prediction accuracy. In the experimental section we demonstrate the performance of our proposed framework with various commonly used pretrained deep networks in the use case of apparent age estimation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.00453>)

[PDF](<https://arxiv.org/pdf/1807.00453.pdf>)