In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and acc
We test the possibility of using a convolutional neural network to infer the inclination angle of a black holedirectlyfrom the incomplete image of the black hole's shadow in theuv-plane. To this end, we develop a proof-of-concept network and use it to explicitly find how the error depends...
Therefore, we experimented with using Convolutional Neural Networks to achieve this goal. 虽然BC7和ASTC可以给纹理做一版压缩,但是随着场景数量和GlobalGIMap数量越来越多,我们也希望有更高的压缩比。所以尝试了使用CNN。 The compression rate of CNN is acceptable, but real-time inference speed became a ...
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Step-by-step code guide to building a Convolutional Neural Network Shreya Rao August 20, 2024 6 min read Deep Dive into LSTMs & xLSTMs by Hand Deep Learning Explore the wisdom of LSTM leading into xLSTMs - a probable competition to the present-day LLMs Srijanie Dey, Ph...
When a concept whitening module is added to a convolutional neural network, the latent space is whitened (that is, decorrelated and normalized) and the axes of the latent space are aligned with known concepts of interest. By experiment, we show that CW can provide us with a much clearer ...
We consider a neural network of L layers. The function f(x) is the network output for an input image x. The activation of layer l is Φl(x). For convolutional layers, Φl(x)∈Rw×h×p, where w is the width, h the height and p the number of channels. The dataset used to ...
Safety-critical applications require transparency in artificial intelligence (AI) components, but widely used convolutional neural networks (CNNs) widely used for perception tasks lack inherent interpretability. Hence, insights into what CNNs have learne
Our proposed approach was then tested against several SL methods, including convolutional neural network (CNN), long-short term memory (LSTM), transformer and the standard clinical method. We found that our model-based RL method was able to export an accurate treatment regimen and outperformed othe...
Recent studies have employed deep learning models such convolutional neural networks [6], [5] or recurrent neural network architectures [3], [5], [4]. In the rest of the section, we describe methods that were trained on publicly available data of different health-related sources and related ...