LHQ
A dataset of 90,000 high-resolution nature landscape images, crawled from Unsplash and Flickr and preprocessed with Mask R-CNN and Inception V3.
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Infinite Image Generation refers to the task of generating an unlimited number of images that belong to a specific distribution or category. It is a challenging task, as it requires the model to capture the underlying patterns and distributions in the data, and generate images that are diverse, yet still follow the same patterns. There are various techniques and algorithms that can be used to perform infinite image generation, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs).
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A dataset of 90,000 high-resolution nature landscape images, crawled from Unsplash and Flickr and preprocessed with Mask R-CNN and Inception V3.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.