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Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-training the network at different resolutions. However re-training a network is rarely cheap and not always viable. To this end, we propose a simple solution to address the train-test distributional shift and enhance the performance of pre-trained models -- which commonly ship as a package with deep learning platformsdoi:10.48550/arxiv.2201.07894 fatcat:umclqps6pfe7zobdqolyiw3evm