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https://github.com/fepegar/unet/blob/9f64483d351b4f7d95c0d871aa7aa587b8fdb21b/unet/unet.py#L32
Monte carlo dropout refers more generally to dropout used during test time, in addition to train time,…
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First of all, thank you for your outstanding work!
The pretext task used in your pre-training phase is point cloud masked reconstruction.
I wonder what's the difference between point cloud comp…
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Thanks for sharing your great job! when i finish the pretext task in my own dataset, i got a checkpoint file, i check the keys in that file, it conbines statedict and boxes, i was confused about the b…
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First of all, thank you for your outstanding work!
The pretext task used in your pre-training phase is point cloud masked reconstruction.
I wonder what's the difference between point cloud comp…
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Test case:
```c#
public virtual Task Client_eval_Union_FirstOrDefault(bool isAsync)
=> AssertFirstOrDefault(
isAsync, cs => cs
.Select(c => ClientSideMethod(c))
…
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Thanks!
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The following issue aims to run the specified test for the current release candidate, report the results, and open new issues for any encountered errors.
## Test information
| …
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HI, great work ! But I have a question regarding how to repeat the experiments.
While getting the mean accuracy, you used the fixed representation and only repeated the logistic regression part. Shal…
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Hi, after every training epoch you test your model on a test set. Why do you load the full 50k training images and not the 10k test images?
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Thank you for your great work. According to your paper, the performance of nearest neighbors in after pretext task can be good, but there may not be cluster structure (please correct me if I am wrong)…