# Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression

Source: [Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression](<https://www.sota2.com/research/paper/68fa6e481e209131cea3ef4c>)

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

Data updated: 2025-10-26T00:54:03.791Z

Authors: Xin Ding, Yongwei Wang, Zuheng Xu, Z. Jane Wang, William J. Welch

Published: 2021-04-07T14:52:49.000Z

## Abstract

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar response variable has been rarely studied, and there exists no KD method applicable to both classification and regression tasks yet. Moreover, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on cGANs, termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 show that we can combine state of the art KD methods with the cGAN-KD framework to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.03164>)

[PDF](<https://arxiv.org/pdf/2104.03164.pdf>)