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Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions. (arXiv:2201.07894v1 [cs.CV])
Jan. 21, 2022, 2:10 a.m. | Touqeer Ahmad, Mohsen Jafarzadeh, Akshay Raj Dhamija, Ryan Rabinowitz, Steve Cruz, Chunchun Li, Terrance E. Boult
cs.CV updates on arXiv.org arxiv.org
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 platforms \eg, PyTorch. …
More from arxiv.org / cs.CV updates on arXiv.org
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