10.2) and modeling on eight classes (Fig. 10.3). It also demonstrated the use of PixelCNN as an image decoder. The generated samples were of average quality depicting the model's capability of capturing the var
Figure1demonstrates how the generator and discriminator work together. The generator aims to deceive the discriminator by providing the synthetically generated image with the objective that it is proven real. The discriminator discerns between genuine and counterfeit images and generates the output signal....
Training-image based geostatistical inversion using a spatial generative adversarial neural network. Water Resources Research, 2018, 54(1): 381-406. 本文引用 [1] [34] WANG N Z, ZHANG D X, CHANG H B, et al. Deep learning of subsurface flow via theory-guided neural network. Journal of ...
Long short-term memory37 and CNN–LSTM were evaluated in combination with the above three decoders. Long short-term memory is used in character-level text modeling. The embedding space from the multicategorical and autoregressive models was still inadequate using either encoder (Supplementary Section...
封面图片:Théâtre D'opéra Spatial, an image generated with Midjourney图片来源:Florian Hirzinger - www.fh-ap.com此图片属于公共领域0. 概述[1] 生成式人工智能(英语:Generative artificial intelligence,或称Generative AI、生成式AI、产生式AI)是一种人工智能系统,能够产生文字、图像或其他媒体以回应提示...
In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for short and medium-range weather forecasts are typically organized into a regular spatial grid structure. This arrangement closely resemb...
Tracks of typhoons are predicted using a generative adversarial network (GAN) with satellite images as inputs. Time series of satellite images of typhoons which occurred in the Korea Peninsula in the past are used to train the neural network. The trained
34 Conditional Image Synthesis with Auxiliary Classifier GANs (AC-GAN) [pdf] 2016 86 35 Improving Variational Inference with Inverse Autoregressive Flow [pdf] 2016 82 36 Generative Image Modeling using Style and Structure Adversarial Networks (S^2GAN) [pdf] 2016 82 37 BEGAN: Boundary Equilibrium ...
convergence is not automatic and the training of GANs necessitates the use of stabilizing techniques[3]. The discriminator is frequently a CNN network, e.g. a ResNet, and the generator an RNN network, e.g. an LSTM, similarly to the encoder and decoder in the VAE-based image-to-text te...
The spatial positions of the fixations were normalized to [0, 1]. Moreover, when training on the Salient360 dataset, input images were downsampled to fit the dimensions of 300 × 600 prior to training. We also subtracted the mean pixel value of the training set from the image's pixels ...