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7 changes: 3 additions & 4 deletions README.md
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Expand Up @@ -11,7 +11,7 @@
<!-- <a href="https://icml.cc/media/PosterPDFs/ICML%202022/a8acc28734d4fe90ea24353d901ae678.png"> <img src="https://img.shields.io/badge/Poster-grey?logo=airplayvideo&logoColor=white" alt="Poster"></a> -->
</p>

This repo contains the sample code of our proposed ```Unleashing Mask (UM)``` and its variant ```Unleashing Mask Adopt Pruning (UMAP)``` to adjust the given well-trained model for OOD detection in our paper: [Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability](https://https://github.com/ZFancy/Unleashing-Mask) (ICML 2023).
This repo contains the sample code of our proposed ```Unleashing Mask (UM)``` and its variant ```Unleashing Mask Adopt Pruning (UMAP)``` to adjust the given well-trained model for OOD detection in our paper: [Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability](https://github.com/ZFancy/Unleashing-Mask) (ICML 2023).
<p align="center"><img src="./figures/framework_overview.jpg" width=100% height=50%></p>
<p align="center"><em>Figure.</em> Framework overview of UM.</p>

Expand Down Expand Up @@ -39,7 +39,7 @@ class CustomLoss(nn.Module):
def forward(self, outputs, labels):
loss = args.beta * self.CrossEntropyLoss(outputs, labels)
if self.criterion != "CrossEntropyLoss":
# args.UM is the estimated loss constriant
# args.UM is the estimated loss constraint
loss = (loss - args.UM).abs() + args.UM
return loss
```
Expand Down Expand Up @@ -73,7 +73,7 @@ criterion = nn.CrossEntropyLoss()
for input, target in data:
...
loss = criterion(model(input), target)
loss = (loss - args.UM).abs() + args.UM # args.UM is the estimated loss constriant
loss = (loss - args.UM).abs() + args.UM # args.UM is the estimated loss constraint
loss.backward()

...
Expand Down Expand Up @@ -130,7 +130,6 @@ python main.py --config configs/smallscale/densenet_cifar10.yaml \
--UM <estimated_loss>\
--energy
```
python main.py --config configs/smallscale/densenet_cifar10.yaml

To experiment with the Outlier Exposure (OE) OOD detection methods. Use flag ```--oe-ood-method``` to control the OE-based methods.

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