A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max-Pooling
نویسندگان
چکیده
منابع مشابه
AdGAP: Advanced Global Average Pooling
Global average pooling (GAP) has been used previously to generate class activation maps. The motivation behind AdGAP comes from the fact that the convolutional filters possess position information of the essential features and hence, combination of the feature maps could help us locate the class instances in an image. Our novel architecture generates promising results and unlike previous method...
متن کاملMultipartite Pooling for Deep Convolutional Neural Networks
We propose a novel pooling strategy that learns how to adaptively rank deep convolutional features for selecting more informative representations. To this end, we exploit discriminative analysis to project the features onto a space spanned by the number of classes in the dataset under study. This maps the notion of labels in the feature space into instances in the projected space. We employ the...
متن کاملMax-Pooling Dropout for Regularization of Convolutional Neural Networks
Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advoc...
متن کاملStochastic Pooling for Regularization of Deep Convolutional Neural Networks
We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined wi...
متن کاملLearned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
In this paper we propose and investigate a novel nonlinear unit, called an Lp unit, for deep neural networks. The proposed Lp unit receives signal from several projections of a subset of units in the layer below and computes the normalized Lp norm. We notice two interesting interpretations of the Lp unit. First, the proposed unit can be understood as a generalization of a number of conventional...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Traitement Du Signal
سال: 2023
ISSN: ['0765-0019', '1958-5608']
DOI: https://doi.org/10.18280/ts.400216