Style transfer aims to render a new artistic image based on a content image and given artwork style. Recent style transfer techniques often suffer structure distortion and artifact problems that abate the quality of stylized images. Motivated by these observations and the previous works, we introduce a novel GAN framework to enhance the aesthetics, faithfulness and flexibility in the style transfer process. The key factor of our model is the Laplacian Pyramid loss that naturally forces the content preservation and the ResidualStyle discriminator block to capture the artwork’s painting style better. In contrast to existing methods that calculate the Euclidean distance between the features of generated image and content image, our Laplacian Pyramid loss better captures the content representation by different frequency bands of the content image. As evaluated by experimental results, our framework surmounts the unrealistic artifacts to synthesize the photorealistic artworks in real-time, hence attaining striking visual effects.
Skin Lesion is a controversial disease all over the world, particularly Melanoma which is a kind of skin cancer. In recent years, there are several methods of using the Convolutional Neural Network and Vision Transformer model have been proposed for the detection and classification of skin images and have achieved competitive results. In this paper, we introduce and demonstrate the efficiency of the Tiny Convolution Contextual Neural Network (TCC Neural Network) which is a tiny model with light-weight architecture than the previous architecture, and have fewer parameter than the popular model for classification of nine lesions from skin images. Our proposal achieves 0.75 on accuracy and 0.55 on F1 score with 5.6 million parameters in the Skin Lesion classification task.
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