To date, Transformer-based frameworks have demonstrated impressive results in single-image super-resolution (SISR).

We denote observed variables with gray color and latent variables with white color. Firstly, we introduce the concept of an uncontrolled style factor . Why do confident examples encourage content-style isolation? Calculate the loss using Eq. 1 and update networks; Output: The inference networks and classifier heads q It's essential to understand that although data augmentation cannot control all style factors, it still offers the benefit of "partial isolation". This approach, therefore, ensures that styles changes don't affect the derived content representation Calculate the loss using Eq. 2 and update networks; Output: The inference networks and classifier heads q Finally, confident and unlabeled examples are used to train the models based on the MixMatch algorithm.

Label noise widely exists in large-scale image datasets.

While 3DGaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a goodinitialization.

However, there still exist many properties of contrastive learning that are not guaranteed.