SlowGuess/NeurIPS_2023_OCR huggingface dataset:
The results in Table S14 (a) extend the results discussed in the previous section (Table S13 (a)). The results indicate that when the models are trained and tested with interpolation tasks generated based on centered image data, RCNP (sta) works better than RCNP (tor). We believe this is because the relative locations calculated based on pixel coordinates on the image canvas are more informative about the absolute location than relative locations calculated on the torus. In other words, while RCNP (sta) and RCNP (tor) do not encode the absolute target location on the image canvas, RCNP (sta) can learn to derive equivalent information based on regularities between context sets.
The image interpolation task becomes translation equivariant on the torus when the models are trained or tested using the image data with random translations. The results reported in Table S14 (b) indicate that using differences calculated on the torus allows RCNP (tor) to generalize between centered and translated images while the other models are not able to make sensible predictions in this OOID task. All models work well when trained with matched data (Table S14 (c)), meaning that training on image data with random translations allows the other models to learn the equivariance which is encoded in RCNP (tor). The best results in Table S14 (c) are observed with ConvCNP, while RCNP (tor) and RCNP (sta) are similar to CNP when  $N = 2$  and better than CNP when more context data is available.
H.3.4 Computation time
The models were trained on a single GPU, and each training run with RCNP took around 3-4 hours, each training run with CNP around 1-2 hours, and each training run with ConvCNP around 4.5-5.5 hours depending on the training data. The results presented in this supplement required 110 training runs with total runtime around 350 hours. In addition other experiments carried out with image data took around 2000 hours.