Vision-aided GAN training involves using pretrained computer vision models in an ensemble of discriminators to improve GAN performance. Linear separability between real and fake samples in pretrained model embeddings is used as a measure to choose the most accurate pretrained models for a dataset.

ViLT is a minimal vision-and-language pre-training transformer model where processing of visual inputs is simplified to just the same convolution-free manner that text inputs are processed. The model-specific components of ViLT require less computation than the transformer component for multimodal interactions. ViLTThe model is pre-trained on the following objectives: image text matching, masked language modeling, and word patch alignment.

ViLT

ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision

Vision-aided GAN

Ensembling Off-the-shelf Models for GAN Training

The hybrid model couples existing macro-meteorological models developed for similar microclimates along with some minimal amount of locally-acquired meteorological and $C_n^2$ data. The hybrid model framework consists of two components, a baseline macro-meteorological model and a machine learning model trained on that baseline macro-meteorological model’s residual error over the locally-acquired training measurements.

Source	Ensembling Off-the-shelf Models for GAN Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com