论文标题
转换一致性正则化 - 图像到图像翻译的半监督范式
Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation
论文作者
论文摘要
标记数据的稀缺性激发了半监督学习方法的发展，这些学习方法从大量未标记的数据以及一些标记的样本中学习。模型在不同输入扰动下的预测之间的一致性正则化，特别是已证明可以在半监督框架中提供最先进的结果。但是，这些方法中的大多数仅限于分类和分割应用程序。我们提出了转换一致性正则化，该一致性正规化探究了图像到图像翻译的更具挑战性的设置，该设置仍未通过半监督算法来探索。该方法引入了一组多样化的几何变换，并强制对模型的未标记数据的预测对这些转换是不变的。我们评估算法对三种不同应用的功效：图像着色，降解和超分辨率。我们的方法具有显着的数据效率，仅需要约10-20％的标记样品才能实现与其完全监督的对应物相似的图像重建。此外，我们还显示了方法在视频处理应用中的有效性，可以利用一些框架的知识来提高电影的其余部分的质量。
Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples. Consistency Regularization between model's predictions under different input perturbations, particularly has shown to provide state-of-the art results in a semi-supervised framework. However, most of these method have been limited to classification and segmentation applications. We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation, which remains unexplored by semi-supervised algorithms. The method introduces a diverse set of geometric transformations and enforces the model's predictions for unlabeled data to be invariant to those transformations. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10 - 20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart. Furthermore, we show the effectiveness of our method in video processing applications, where knowledge from a few frames can be leveraged to enhance the quality of the rest of the movie.
