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> Texture is a more informative feature than shape in the datasets used. But the insight is that although both features are informative, classical training app
by Majromax 7y ago
> Texture is a more informative feature than shape in the datasets used.
But the insight is that although both features are informative, classical training approaches result in networks that over-fit on texture information and ignore shape information.
Table 2 of the discussed paper finds that joint training with "Stylized ImageNet" (IN images after a style transfer) and classic IN followed by a fine-tuning pass on IN results in improved classification accuracy over IN training alone.
- skgoa 7y agoWhich proves exactly what the top level comment claimed: the distribution of examples in the dataset was incorrect for the learning task.
- yarg 7y agoNot necessarily. Textures are high frequency low abstraction data forms, shapes are low frequency high abstraction data forms. The former are easier to learn for multiple reasons: The nature of higher frequency data means that you have more samples. (There are far more furry textures in a picture of a cat than there are toes, paws, legs etc.) Low abstraction data forms are also learnt in earlier layers of the network - more abstract forms are resolved through the understanding of how less abstract forms group together, making the low abstraction forms a prerequisite to more in depth learning.