Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?

9 Nov 2017Grigorios KalliatakisAnca SticlaruGeorge StamatiadisShoaib EhsanAles LeonardisJuergen GallKlaus D. McDonald-Maier

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on three challenging real-world material databases show that the best performing pre-trained convolutional neural network (CNN) architectures can achieve up to 91.03% mean average precision when classifying materials in cross-dataset scenarios... (read more)

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