For instance, you could train an autoencoder on grainy images and then use the trained model to remove the grain/noise from the image. Let’s take a look at the architecture of an autoencoder. We’ll ...

Simple Neural Network is feed-forward wherein info information ventures just in one direction.i.e. the information passes from input layers to hidden layers finally to the output layers. Recurrent ...

We propose an adaptive 1D convolutional autoencoder architecture that can compress and recover spectral ... We show the high transferability and generalizability of our A1D-CAE model for compression ...

Explore how Sparc3D transforms 2D images into detailed 3D models with AI-powered efficiency and precision. Discover more.

The propsoed model architecture is presented for both RGB and Multispectral ... title = {Hybrid Spatial–Spectral Autoencoder Models for Lossy Satellite Image Compression}, journal = {Journal of ...

We propose an adaptive 1D convolutional autoencoder architecture for lossy hyperspectral data compression with the property of portability to unknown spectral signatures of different sensors. In our ...

SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the ...

They can be easily installed via pip or conda. In the notebook folder, one can find the *.ipynb files corresponding to the tutorials to run the models in Google Colab without installing the package.