# Decoder Denoising Pretraining for Semantic Segmentation

**Authors:** Emmanuel Brempong Asiedu, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer, Mohammad Norouzi  
**arXiv:** [2205.11423](https://arxiv.org/abs/2205.11423)  
**Date:** 2022-05-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2205.11423v1/

## Key points

- Semantic segmentation involves labeling each pixel in an image with its corresponding class
- Acquiring accurate and comprehensive labels for semantic segmentation is expensive and time-consuming
- Pretraining techniques are commonly used to enhance label-efficiency of segmentation models
- Traditional approach pretrains the encoder as a classifier while the decoder is randomly initialized
- Random initialization of the decoder may not be optimal, especially with few labeled examples available
- Authors propose "decoder denoising pretraining" as a novel approach to improve semantic segmentation performance
- Encoder undergoes supervised pretraining as a classifier using labeled data
- Decoder is pretrained using denoising techniques on large-scale datasets like ImageNet
- Combination of encoder and decoder pretraining allows better utilization of limited labeled examples and enhances overall performance
- Experiments conducted on benchmark datasets show that decoder denoising pretraining achieves state-of-the-art performance in label-efficient semantic segmentation
- Outperforms traditional encoder-only supervised pretraining methods by a significant margin despite its simplicity compared to other approaches
- Leveraging large-scale datasets like ImageNet for decoder pretraining effectively improves semantic segmentation results without extensive labeled data requirement

## Summary

In the field of computer vision, semantic segmentation is a crucial task that involves labeling each pixel in an image with its corresponding class. However, acquiring accurate and comprehensive labels for semantic segmentation is expensive and time-consuming. To address this issue, researchers commonly employ pretraining techniques to enhance the label-efficiency of segmentation models. Traditionally, the encoder of a segmentation model is pretrained as a classifier, while the decoder is randomly initialized. However, recent studies suggest that random initialization of the decoder may not be optimal, particularly when only a few labeled examples are available. In light of this, the authors propose a novel approach called "decoder denoising pretraining" for improving semantic segmentation performance. The proposed method involves pretraining both the encoder and decoder components of the segmentation model. Specifically, the encoder undergoes supervised pretraining as a classifier using labeled data. Meanwhile, the decoder is pretrained using denoising techniques on large-scale datasets like ImageNet. This combination allows for better utilization of limited labeled examples and enhances overall performance. The authors conducted experiments to evaluate their approach on various benchmark datasets such as Cityscapes, Pascal Context, and ADE20K. The results demonstrate that decoder denoising pretraining achieves state-of-the-art performance in terms of label-efficient semantic segmentation. Notably, it outperforms traditional encoder-only supervised pretraining methods by a significant margin despite its simplicity compared to other sophisticated approaches. By leveraging large-scale datasets like ImageNet for decoder pretraining, this method effectively improves semantic segmentation results without requiring extensive labeled data. Overall, this research highlights the importance of considering both encoder and decoder components in pretraining strategies for semantic segmentation models. The proposed decoder denoising approach proves to be highly effective in enhancing label efficiency and achieving state-of-the art results on various challenging datasets.