Second-Order Pooling for Graph Neural Networks
نویسندگان
چکیده
منابع مشابه
Second-order Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image. In the past, however, second-order statistics computed from handcrafted features, e.g., covarian...
متن کاملStatistically Motivated Second Order Pooling
Second-order pooling, a.k.a. bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce m...
متن کاملSecond-order Temporal Pooling for Action Recognition
Most successful deep learning models for action recognition generate predictions for short video clips, which are later aggregated into a longer time-frame action descriptor by computing a statistic over these predictions. Zeroth (max) or first order (average) statistic are commonly used. In this paper, we explore the benefits of using second-order statistics. Specifically, we propose a novel e...
متن کاملSecond-order Optimization for Neural Networks
Second-order Optimization for Neural Networks James Martens Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2016 Neural networks are an important class of highly flexible and powerful models inspired by the structure of the brain. They consist of a sequence of interconnected layers, each comprised of basic computational units similar to the gates of a classica...
متن کاملSemantic Segmentation with Second-Order Pooling
Feature extraction, coding and pooling, are important components on many contemporary object recognition paradigms. In this paper we explore novel pooling techniques that encode the second-order statistics of local descriptors inside a region. To achieve this effect, we introduce multiplicative second-order analogues of average and maxpooling that together with appropriate non-linearities lead ...
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: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2020
ISSN: 0162-8828,2160-9292,1939-3539
DOI: 10.1109/tpami.2020.2999032