Live Cell Histology: Extracting latent features from label-free live cell images using Adversarial Autoencoders
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Updated
May 29, 2026 - Lua
Live Cell Histology: Extracting latent features from label-free live cell images using Adversarial Autoencoders
Towards Generative Modeling from (variational) Autoencoder to DCGAN
Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore someti…
Pytorch implementation of Masked Autoencoder I
Filtering out the noise presented in the image by auto-enconder algorithm in TensorFow and Keras. Rare images, unclean crime images,medical noise images can be denoised and find out the desired outcome by using auto-encoders.
Colorizes grayscale images using a loaded model and displays original and predicted colorized versions.
Uma abordagem prática para construção de autoencoders convolucionais.
In this research work, unsupervised abnormality has been detected by using intelligent and heterogeneous autonomous systems.
Live Cell Histology: Extracting latent features from label-free live cell images using Adversarial Autoencoders
vanilla and convolutional autoencoder for generating mnist images
Person Segmentation using custom Autoencoder architecture and evaluation using IoU and Dice metrics, will also include Unet architecture in the future.
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