Decoder Denoising Pretraining for Semantic Segmentation

• URL: http://arxiv.org/abs/2205.11423v1
• Date: Mon, 23 May 2022 16:08:31 GMT
• Title: Decoder Denoising Pretraining for Semantic Segmentation
• Authors: Emmanuel Brempong Asiedu, Simon Kornblith, Ting Chen, Niki Parmar, Matthias Minderer and Mohammad Norouzi
• Abstract summary: We propose a decoder pretraining approach based on denoising. We find that decoder denoising pretraining on the ImageNet dataset strongly outperforms encoder-only supervised pretraining.
• Score: 46.23441959230505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation labels are expensive and time consuming to acquire. Hence, pretraining is commonly used to improve the label-efficiency of segmentation models. Typically, the encoder of a segmentation model is pretrained as a classifier and the decoder is randomly initialized. Here, we argue that random initialization of the decoder can be suboptimal, especially when few labeled examples are available. We propose a decoder pretraining approach based on denoising, which can be combined with supervised pretraining of the encoder. We find that decoder denoising pretraining on the ImageNet dataset strongly outperforms encoder-only supervised pretraining. Despite its simplicity, decoder denoising pretraining achieves state-of-the-art results on label-efficient semantic segmentation and offers considerable gains on the Cityscapes, Pascal Context, and ADE20K datasets.

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