태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Noisy semantic image synthesis involves generating realistic images from noisy semantic maps. This task evaluates the robustness of a method when given noisy input from users in real-world applications.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [DS] employs downsampled semantic maps that are resized by nearest-neighbor interpolation, simulating human errors e.g., jagged edges and coarse/low resolution user inputs.
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Edge] masks the edges of instances with an unlabeled class, imitating incomplete annotations around edges, especially between instances.
A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Random] randomly adds an unlabeled class to the semantic maps, mimicing unintended user error and extreme random noise.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.