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View Code? Open in Web Editor NEWPytorch implementation of COIN, a framework for compression with implicit neural representations ๐ธ
License: MIT License
Pytorch implementation of COIN, a framework for compression with implicit neural representations ๐ธ
License: MIT License
In the training process 51~125 lines in main.py, a single model trains images one by one.
However, i thought it is better to train multiple models corresponding to each image, which means we have each model per image, because it causes more overfitting.
Why didn't you do train models like such approach?
Also, Let's say a single model with multiple images is better. But, why you fed images to a single model one by one? There is another way such as mini-batch training(batchsize = 4 or 8 or 16 ...).
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