1 Introduction
Table 4: Transfer learning accuracy using the representations learned using different models trained with 10000 instances from the MNIST dataset.
['[BOLD] Model', '[BOLD] Left to Right', '[BOLD] Right to Left']
Single view corresponds to the classifier trained and tested on same view. This is the upper bound for the performance of any transfer learning algorithm. Once again, we see that CorrNet performs significantly better than the other models. To verify that this holds even when we decrease the data for learning common representation to 10000 images. The results as reported in