On the Performance of GoogLeNet and AlexNet Applied to Sketches

We however show that Convolutional Neural Networks (CNN) are considered the both networks are largely unable to recognize most tested state-of-the-art model in image recognition tasks. Part of a subjects, indicating that the learned representations are quite deep learning approach to machine learning, CNN have been different from that of humans. We argue that such approach deployed successfully in a variety of applications, including can be useful to assess classifiers' generalization capabilities, face recognition (Lawrence et al. 1997), object classification in particular regarding to the abstraction level of learned (Szegedy et al. 2014) and generating scene descriptions representations. (Pinheiro and Collobert 2013). This success can be partly The main contribution of this work is to put forward an attributed to advances in learning algorithms for deep architectures image recognition task where current state-of-the-art models and partly to large labeled data sets made available, differ significantly in performance when compared to humans.