Comments (1)
This seems to stabilize test performance, I noticed that otherwise the variance was very large between runs otherwise, which made it hard to report with.
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Related Issues (20)
- Can you mention the hyper-parameters used to reproduce baseline(random) cifar10 results?
- WAE implementation details for segmentation
- Is there a reason behind removing bias from conv2d transpose HOT 1
- Question about the performance on CIFAR100 HOT 1
- Total time HOT 4
- Cannot achieve the performance in the paper HOT 5
- Core-set baseline HOT 1
- Problem about the shape of the VAE's output HOT 3
- Network Architecture HOT 1
- Question-
- GPU implementation HOT 2
- Transductive representation learning is time-consuming
- CIFAR 100 Performance discrepancy HOT 2
- The loss of the discriminator cannot be reduced HOT 1
- Resnet18 architecture
- ImageNet dataset: resize the image or change the dimension of z HOT 1
- Regarding VAE architecture. HOT 2
- How to apply VAAL for semantic segmentation?
- Eval mode for VAE and Discriminator
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