Description:
This project applies the Elastic Deformation technique to the training data of a GAN model by inserting MNIST data with elastic transformations. The goal is to generate diverse images during the image generation process.
Data: MNIST
Elastic Deformation :
def elastic_deformation(image, alpha, sigma):
random_state = np.random.RandomState(None)
shape = image.shape
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1))
distorted_image = map_coordinates(image.reshape(shape), indices, order=1, mode='reflect')
return distorted_image.reshape(shape)
Elastic Deformation MNist image:
Description:
In this project, we experiment with applying a new activation function with learnable parameters, alpha and beta, in place of the traditional ReLU function in Variational AutoEncoders.
Data: MNIST
abReLU Function:
class abReLU(nn.Module):
def __init__(self, init_a=7.0, init_b=0.0):
super(abReLU, self).__init__()
self.a = nn.Parameter(torch.tensor(init_a, dtype=torch.float32))
self.b = nn.Parameter(torch.tensor(init_b, dtype=torch.float32))
def forward(self, x):
return torch.where(x >= self.b, self.a * (x - self.b), torch.zeros_like(x))
Visual:
Description:
This project focuses on studying Denoising Diffusion Probabilistic Models (DDPM) using CIFAR-10 data.
Data: CIFAR-10
Generated Results:
