Intrinsic image decomposition is the classical task of mapping image to albedo. The WHDR dataset allows methods to be evaluated by comparing predictions to human judgements ("lighter", "same as", "darker"). The best modern intrinsic image methods learn a map from image to albedo using rendered models and human judgements. This is convenient for practical methods, but cannot explain how a visual agent without geometric, surface and illumination models and a renderer could learn to recover intrinsic images. This paper describes a method that learns intrinsic image decomposition without seeing WHDR annotations, rendered data, or ground truth data. The method relies on paradigms - fake albedos and fake shading fields - together with a novel smoothing procedure that ensures good behavior at short scales on real images. Long scale error is controlled by averaging. Our method achieves WHDR scores competitive with those of strong recent methods allowed to see training WHDR annotations, rendered data, and ground truth data. Because our method is unsupervised, we can compute estimates of the test/train variance of WHDR scores; these are quite large, and it is unsafe to rely small differences in reported WHDR.

翻译：内在图像分解是将图像映射到反光度的经典任务。 WHDR 数据集允许通过将预测与人类判断( “ lighter ”, “same as”, “ darker ” ) 进行比较来评估方法。 最佳现代内在图像方法使用模型和人类判断从图像到反光度学习地图。 这是实用方法的方便, 但无法解释没有几何、 表面和光化模型的视觉代理可以如何学习恢复内在图像。 本文描述了一种方法, 它可以学习内在图像分解, 而不会看到 WHDR 说明、 提供的数据或地面真相数据。 这种方法依赖于模式 — 假的反光度和假的阴影场, 以及一种新颖的平滑程序, 确保在真实图像的短尺度上良好行为。 长期的错误由平均控制。 我们的方法能让WHDRDR 与那些最近能够查看的强力方法相比具有竞争力。 由于我们的方法没有超度, 我们可以对 WHDRDDz 的测试/ 差异进行估算。 这些差异很小。