Results 11 to 20 of about 86,091 (310)
Analysis of (sub-)Riemannian PDE-G-CNNs [PDF]
, 2023 Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been hard ...
Smets, Bart M. N. +4 more
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Geometric Adaptations of PDE-G-CNNs [PDF]
, 2023 Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. The recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalizes G-CNNs while simultaneously reducing network complexity and
Oliván Bescós, Javier +3 more
core +1 more source
Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition
Sensors, 2020 On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs.
Gabriel Villalonga +2 more
doaj +1 more source
Simulating CRF with CNN for CNN
CoRR, 2019 Combining CNN with CRF for modeling dependencies between pixel labels is a popular research direction. This task is far from trivial, especially if end-to-end training is desired. In this paper, we propose a novel simple approach to CNN+CRF combination.
Lena Gorelick, Olga Veksler
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GIScience & Remote Sensing, 2020 Rice mapping with remote sensing imagery provides an alternative means for estimating crop-yield and performing land management due to the large geographical coverage and low cost of remotely sensed data.
Shuang Zhao +5 more
doaj +1 more source
Going Deeper with Dense Connectedly Convolutional Neural Networks for Multispectral Pansharpening
Remote Sensing, 2019 In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening).
Dong Wang +4 more
doaj +1 more source
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters [PDF]
Genes, 2020 A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired ...
Muhammad Shujaat +3 more
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CoRR, 2016 It has long been recognized that the invariance and equivariance properties of a representation are critically important for success in many vision tasks. In this paper we present Steerable Convolutional Neural Networks, an efficient and flexible class of equivariant convolutional networks.
Taco S. Cohen, Max Welling
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Botulinum Toxin Suppression of CNS Network Activity In Vitro
Journal of Toxicology, 2014 The botulinum toxins are potent agents which disrupt synaptic transmission. While the standard method for BoNT detection and quantification is based on the mouse lethality assay, we have examined whether alterations in cultured neuronal network activity ...
Joseph J. Pancrazio +3 more
doaj +1 more source
2017 IEEE International Conference on Computer Vision (ICCV), 2017 The deep Convolutional Neural Network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following basic principles such as increasing the depth and constructing highway connections, researchers have manually designed a lot of fixed network structures and verified their effectiveness. In this paper, we discuss the possibility of
Lingxi Xie, Alan L. Yuille
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