Results

In addition to CYCLIP described in 2, we train two more instantiations of it by keeping either of the two consistency regularizers active in the loss objective (Eq. The instantiation trained by setting λ1 = 0and λ2 = 0.5is termed as C-CYCLIP as only cross-modal consistency regularizer term is added to the loss objective. Similarly, we get I-CYCLIP where only in-modal consistency regularizer is added to the loss by setting λ1 = 0.5 and λ2 = 0. We evaluate C-CYCLIP and I-CYCLIP on most of the experiments discussed in the main text to understand their zero-shot transfer ability on standard datasets and robustness to natural distribution shifts. A.1 Zero-shot Transfer Table 7 presents our results of the zero-shot transfer experiment described in 3.1. We find that CYCLIP outperforms its sub-variants and the CLIP model on the ImageNet1K dataset.