Sign language recognition (SLR) has recently achieved a breakthrough in
performance thanks to deep neural networks trained on large annotated sign
datasets. Of the many different sign languages, these annotated datasets are
only available for a select few. Since acquiring gloss-level labels on sign
language videos is difficult, learning by transferring knowledge from existing
annotated sources is useful for recognition in under-resourced sign languages.
This study provides a publicly available cross-dataset transfer learning
benchmark from two existing public Turkish SLR datasets. We use a temporal
graph convolution-based sign language recognition approach to evaluate five
supervised transfer learning approaches and experiment with closed-set and
partial-set cross-dataset transfer learning. Experiments demonstrate that
improvement over finetuning based transfer learning is possible with
specialized supervised transfer learning methods.

该研究提供了一个公开的跨数据集转移学习基准，通过两个公开的土耳其手语识别数据集进行评估，使用基于时间图卷积的手语识别方法研究了五种有监督的转移学习方法，实验证明专业的有监督转移学习方法可以在闭集和部分集的数据集转移学习中获得比微调更好的性能。

跨数据集的转移学习在资源有限的数据集中的孤立手语识别

Transfer Learning for Cross-dataset Isolated Sign Language Recognition  in Under-Resourced Datasets

Cancer diagnosis is a well-studied problem in machine learning since early
detection of cancer is often the determining factor in prognosis. Supervised
deep learning achieves excellent results in cancer image classification,
usually through transfer learning. However, these models require large amounts
of labelled data and for several types of cancer, large labelled datasets do
not exist. In this paper, we demonstrate that a model pre-trained using a
self-supervised learning algorithm known as Barlow Twins can outperform the
conventional supervised transfer learning pipeline. We juxtapose two base
models: i) pretrained in a supervised fashion on ImageNet; ii) pretrained in a
self-supervised fashion on ImageNet. Both are subsequently fine tuned on a
small labelled skin lesion dataset and evaluated on a large test set. We
achieve a mean test accuracy of 70\% for self-supervised transfer in comparison
to 66\% for supervised transfer. Interestingly, boosting performance further is
possible by self-supervised pretraining a second time (on unlabelled skin
lesion images) before subsequent fine tuning. This hints at an alternative path
to collecting more labelled data in settings where this is challenging - namely
just collecting more unlabelled images. Our framework is applicable to cancer
image classification models in the low-labelled data regime.

在本文中，我们展示了使用一种称为 Barlow Twins 的自我监督学习算法进行预训练的模型可以胜过常规的监督转移学习流程，该模型在小型标记皮肤病变数据集上进行微调后，在大型测试集中取得了 70% 的平均测试准确率，相比于监督转移学习的 66%。这暗示在标记困难的环境中收集更多未标记图像可能是一条获取更多标记数据的替代途径。我们的框架适用于低标记数据情境下的癌症图像分类模型。