Capsule Network Performance on Complex Data
نویسندگان
چکیده
In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN’s have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN’s: their failure to take into account of important spatial hierarchies between features, and their lack of rotational invariance [1]. As long as certain key features of an object are present in the test data, CNN’s classify the test data as the object, disregarding features’ relative spatial orientation to each other. This causes false positives. The lack of rotational invariance in CNN’s would cause the network to incorrectly assign the object another label, causing false negatives. To address this concern, Hinton et al. propose a novel type of neural network using the concept of capsules in a recent paper. With the use of dynamic routing and reconstruction regularization, the capsule network model would be both rotation invariant and spatially aware. [1]
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ورودعنوان ژورنال:
- CoRR
دوره abs/1712.03480 شماره
صفحات -
تاریخ انتشار 2017