Results 21 to 30 of about 102,874 (306)
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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CNN+CNN: Convolutional Decoders for Image Captioning
CoRR, 2018 Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field.
Qingzhong Wang, Antoni B. Chan
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, 2023 Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which ...
Makadia, Ameesh +2 more
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Open Chemistry, 2023 Ciprofloxacin (CIP) belongs to the fluoroquinolone antibiotic family. It is mostly used for the treatment of bacterial infections and highly recalcitrant to naturally decompose.
Tamyiz Muchammad, Doong Ruey-an
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CNNs Avoid the Curse of Dimensionality by Learning on Patches
IEEE Open Journal of Signal Processing, 2023 Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori ...
Vamshi C. Madala +2 more
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Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
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Evaluating 'Graphical Perception' with CNNs
, 2018 Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks?
James Tompkin +2 more
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging
Engineering Proceedings, 2023 Convolutional Neural Networks (CNNs) are kinds of deep learning models that were created primarily for processing and evaluating visual input, which makes them extremely applicable in the field of medical imaging.
Satyam Tiwari +5 more
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, 2022 The CNNs block consists of three 3D convolutional layers, with kernels of sizes 3×3×3, each of which is followed by a max-pooling layer with a kernel of size 2×2×2.
Rogers F. Silva (11986044) +10 more
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How explainable are adversarially-robust CNNs? [PDF]
, 2023 Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria have been studied independently, their relationship is unknown.
Chen, Peijie +3 more
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