Comments (1)
Hello,
we used the same hyper-parameters for 1% and 10% in the semi-supervised results.
The difference between the numbers reported in the paper (acc1 = 69.7%, acc5= 89.3%) and the numbers for the open-sourced model (acc1 = 68.89%, acc5 = 88.966%) are small enough that I would put them down to the randomness of training. However, we really should (and we plan to) perform multiple training runs of Barlow Twins so that we can provide error bars for our results.
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Related Issues (20)
- Issue loading checkpoint.pth file HOT 1
- Augmentation Distribution HOT 1
- Will BarlowTwins overfit on the training data?
- When using the own dataset, loss is around 10,000 HOT 8
- About the last normalization layer HOT 1
- Dose we can add distorted image, like Y_a, Y_b, Y_c?
- A question on the BT loss with Batch Norm layers HOT 4
- Start index for each epoch HOT 3
- weight update for step=0,epoch=0 get's missed. HOT 2
- Applications on one-dimensional signal datasets HOT 4
- Where could we find the "reproduced version" of the other SSL methods HOT 1
- Question about Fig. 4 in the paper HOT 4
- Quality of Embeddings
- Possible bug on the loss computation HOT 1
- Error in saving resnet50.pth HOT 2
- efficiency proposal HOT 1
- NaN's introduced during training.
- providing the linear ImageNet classifier weights
- Pre-training model for CIFAR
- Why c.div_(self.args.batch_size) is needed? HOT 1
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