Dec 13, 2020 � We propose a simple and effective contrastive learning-based training strategy in which we first pretrain the network using a pixel-wise, label-based�...
In contrast, we focus on supervised contrastive learning, and make use of the labels in both pretraining and fine-tuning stages. We perform experiments on two�...
This repo contains standalone implementation of two supervised pixel contrastive losses. These losses are a slightly modified versions of losses used to train�...
Collecting labeled data for the task of semantic segmen- tation is expensive and time-consuming, as it requires dense pixel-level annotations.
We propose a simple and effective contrastive learning-based training strategy in which we first pretrain the network using a pixel-wise, label-based�...
A simple and effective contrastive learning-based training strategy in which the network is pretrain the network using a pixel-wise, label-based contrastive�...
This is the pytorch implementation of paper "Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation".
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Supplementary Material - Contrastive Learning for Label Efficient Semantic ... Contrastive pretraining improves the segmentation results by reducing the�...
The key component of contrastive learning is to use similar samples as positive samples and dissimilar samples as negative samples and to compare the positive�...
Sep 15, 2021 � In this paper, we establish that by including the limited label in formation in the pre-training phase, it is possible to boost the performance�...
