Interpolating convolutional neural networks using batch normalization
Perceiving a visual concept as a mixture of learned ones is natural for humans, aiding them to grasp new concepts and strengthening old ones. For all their power and recent success, deep convolutional networks do not have this ability. Inspired by recent work on universal representations for neural...
Huvudupphovsmän: | Data, G, Ngu, K, Murray, D, Prisacariu, V |
---|---|
Materialtyp: | Conference item |
Publicerad: |
Springer
2018
|
Liknande verk
-
Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
av: Muhammad Atif Bilal, et al.
Publicerad: (2022-07-01) -
Convolution neural network with batch normalization and inception-residual modules for Android malware classification
av: TianYue Liu, et al.
Publicerad: (2022-08-01) -
Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis
av: Chao Fu, et al.
Publicerad: (2020-01-01) -
Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling
av: Shui-Hua Wang, et al.
Publicerad: (2018-11-01) -
Reconstruction and Localization of Tumors in Breast Optical Imaging via Convolution Neural Network Based on Batch Normalization Layers
av: Nazish Murad, et al.
Publicerad: (2022-01-01)