# GAN-Knowledge Distillation for one-stage Object Detection

Source: [GAN-Knowledge Distillation for one-stage Object Detection](<https://www.sota2.com/research/paper/68fa65fd1e209131cea0dc41>)

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

Data updated: 2025-10-25T21:40:30.056Z

Authors: Wei Hong, Jin ke Yu Fan Zong

Published: 2019-06-20T07:10:06.000Z

## Abstract

Convolutional neural networks have a significant improvement in the accuracy of Object detection. As convolutional neural networks become deeper, the accuracy of detection is also obviously improved, and more floating-point calculations are needed. Many researchers use the knowledge distillation method to improve the accuracy of student networks by transferring knowledge from a deeper and larger teachers network to a small student network, in object detection. Most methods of knowledge distillation need to designed complex cost functions and they are aimed at the two-stage object detection algorithm. This paper proposes a clean and effective knowledge distillation method for the one-stage object detection. The feature maps generated by teacher network and student network are used as true samples and fake samples respectively, and generate adversarial training for both to improve the performance of the student network in one-stage object detection.

## 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/1906.08467>)

[PDF](<https://arxiv.org/pdf/1906.08467.pdf>)