Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decoder
This work addresses the problem of optimizing transfer learning for segmentation tasks in computer vision, offering incremental improvements by identifying more effective encoder reuse strategies.
The study found that transfer learning the encoder is beneficial for segmentation tasks, while transfer learning the decoder does not improve overall performance, with ImageNet-pretrained encoders being suboptimal and a contrastive self-supervised approach proposed for better encoders.
It is common practice to reuse models initially trained on different data to increase downstream task performance. Especially in the computer vision domain, ImageNet-pretrained weights have been successfully used for various tasks. In this work, we investigate the impact of transfer learning for segmentation problems, being pixel-wise classification problems that can be tackled with encoder-decoder architectures. We find that transfer learning the decoder does not help downstream segmentation tasks, while transfer learning the encoder is truly beneficial. We demonstrate that pretrained weights for a decoder may yield faster convergence, but they do not improve the overall model performance as one can obtain equivalent results with randomly initialized decoders. However, we show that it is more effective to reuse encoder weights trained on a segmentation or reconstruction task than reusing encoder weights trained on classification tasks. This finding implicates that using ImageNet-pretrained encoders for downstream segmentation problems is suboptimal. We also propose a contrastive self-supervised approach with multiple self-reconstruction tasks, which provides encoders that are suitable for transfer learning in segmentation problems in the absence of segmentation labels.