Deep learning based methods of WM tract segmentation have been proposed, which greatly improve the accuracy of the segmentation. However, the training of the deep networks usually requires a large number of man
(PIRL, pronounced as "pearl") that learns invariant representations based on pretext tasks. We use PIRL with a commonly used pretext task that involves solving jigsaw puzzles. We find that PIRL substantially improves the semantic quality of the learned image representations. Our approach sets a ...
In this paper, we consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data, which demonstrates the state-of-the-art performance in classification task. Despite the reported results, such factors as the complexity of ...
We outperform state-of-the-art methods, in particular +8.9% on CIFAR20, and INSERT HERE on STL10 in terms of classification accuracy.InstallationConda installationconda env create -f env.yml TrainingTo run training without pretext task, fill the config file. Example of detailed config file for...
Our training approach is based on a min-max scheme which reduces overfitting via an adversarial objective and thus optimizes for a more generalizable surrogate model. Our proposed attack is complimentary to the adversarial pixel restoration and is independent of any task specific objective as it can...
In this paper, we show that feature transformations within CNNs can also be regarded as supervisory signals to construct the self-supervised task, called internal pretext task. And such a task can be applied for the enhancement of supervised learning. Specifically, we first transform the internal...
And then, we design three complementary pretext tasks, i.e., local-local self-supervised contrastive learning task, local-context self-supervised contrastive learning task and local-global self-supervised contrastive learning task, which can make model have a deeper understanding of the interaction ...
In auxiliary pretext task methods, learning features using colorization is suitable for segmentation, while predicting the context works well for detection. Figure 15 shows the accuracy of the ImageNet Top-1 linear classifiers, which were trained on feature representations created via self-supervised ...
Top-performing contrastive learning methods, such as SimCLR [26] and MoCo [30], utilize instance discrimination as a pretext task, which has been demonstrated to outperform its supervised counterparts on downstream tasks. Instance discrimination methods train the network so that two augmented versions ...