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unsupervised contrastive loss #522
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Refer to file structure in #518
Codecov Report
@@ Coverage Diff @@
## staging #522 +/- ##
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Coverage 90.09% 90.09%
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Files 7 7
Lines 404 404
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Hits 364 364
Misses 40 40 Continue to review full report at Codecov.
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@Dante-Basile You need to black
format the files.
@jdey4 do you think this is an ideal way to separate supervised & unsupervised? Or should there be separate folders in benchmarks
?
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- Your notebooks have unnecessary outputs & empty cells. Remove them.
- Remove commented lines of code
Reference issue
#520
Type of change
This contains the loss function and a basic ResNet50 demo demonstrating training and decreasing loss on CIFAR-10.
What does this implement/fix?
This demonstrates that the ResNet50 network can effectively optimize the loss of the unsupervised contrastive loss function. Future experiments will examine the accuracy of the trained network.
Additional information
NDD 2021