Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is still much to be explored in terms of their robustness to the variations of specific visual factors.

We use a variety of evaluation metrics to diagnose the effect that training with instance selection has on the learned distribution. In all cases where a reference distribution is required we usethe original training distribution,and not the distribution produced after instance selection. Inception Score (IS) [24] evaluates samples by extracting class probabilities from an ImageNet pretrained Inceptionv3 classifier and measuring the distribution of outputs over all samples. Classification Accuracy Score (CAS) [23, 25] was introduced for evaluating the usefulness of conditional generativemodels for augmenting downstream tasks such as image classification. Model Params (M) Batch Size Retention Ratio(%) IS FID P R D C BigGAN 52.54 512 100 25.43 10.55 - - - FQ-BigGAN 52.54 512 100 25.96 9.67 - - - - The truncation trick isasimple and popular technique which isused toincrease thevisual fidelity of samples from a GAN at the expense of reduced diversity [2].

The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Y et, the use of AI in Universal Algebra (UA)--one of the fields laying the foundations of modern mathematics--is still completely unexplored.