Cell Nuclei Segmentation using VGG16-UNET And Double-UNETImage Segmentation is the process of dividing an image into segments in order to make use of important segments for processing the image…Aug 14, 2021Aug 14, 2021
Retina Blood Vessel Segmentation using VGG16-UNETThis is a continuation from the previous post.Aug 14, 2021Aug 14, 2021
Retina Blood Vessel Segmentation using UNETImage Segmentation is the process of dividing an image into segments in order to make use of important segments for processing the image…Aug 14, 2021Aug 14, 2021
Cycle GAN ArchitectureImage-To-Image translation is the process of conversion of source image to target image. It requires specialized models and custom loss…Aug 8, 2021Aug 8, 2021
Pix2Pix GAN ArchitectureThe conversion of source image to target image is known as Image-to-Image translation. It requires specialized models and custom loss…Aug 7, 2021Aug 7, 2021
Semi-Supervised GAN for MNIST Handwritten DigitsSemi-Supervised GAN involves training of a supervised discriminator, unsupervised discriminator and a generator model simultaneously. It…Aug 5, 2021Aug 5, 2021
Auxiliary GAN for MNIST Handwritten DigitsGenerative Adversarial networks train deep convolutional neural networks for generating images. GAN requires a discriminator model for…Aug 4, 2021Aug 4, 2021
Conditional GAN for MNIST Handwritten DigitsGenerative Adversarial Networks includes a generator model which is capable of generating new plausible fake samples that can be considered…Aug 3, 2021Aug 3, 2021
Deep Convolutional GAN for MNIST Handwritten digitsGenerative Adversarial networks train deep convolutional neural networks for generating images. GAN requires a discriminator model for…Aug 1, 2021Aug 1, 2021
1D GAN for Sine Wave functionGenerative Adversarial Networks includes a generator model which is capable of generating new plausible fake samples that can be considered…Aug 1, 2021Aug 1, 2021
