XLearning-SCU / 2022-CVPR-AirNet

PyTorch implementation for All-In-One Image Restoration for Unknown Corruption (AirNet) (CVPR 2022)

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About negative samples in queue

BZboys opened this issue · comments

Thanks for your great code. I'm a little confused about the negative sample setting. There are five tasks: {'denoise_15': 0, 'denoise_25': 1, 'denoise_50': 2, 'derain': 3, 'dehaze': 4}, and the queue size equals to 256*3. So, how do you ensure that the queue is filled with negative samples that are different from the current degradation?

Thanks for you valuable suggestions, we do not specially pick out the false negative pairs in the queue, for they are widely existed in the contrastive learning. In the next version, we will try to re-train our model by removing the false negative pairs.