**UFLoss**, or **Unsupervised Feature Loss**, is a patch-based unsupervised learned feature loss for deep learning (DL) based reconstructions. The UFLoss provides instance-level discrimination by mapping similar instances to similar low-dimensional feature vectors using a pre-trained mapping network (UFLoss Network). The rationale of using features from large-patches (typically 40×40 pixels for a 300×300 pixels image) is that we want the UFLoss to capture mid-level structural and semantic features instead of using small patches (typically around 10×10 pixels), which only contain local edge information. On the other hand, the authors avoid using global features due to the fact that the training set (typically around 5000 slices) is usually not large enough to capture common and general features at a large-image scale.

**ReLU6** is a modification of the [rectified linear unit](https://paperswithcode.com/method/relu) where we limit the activation to a maximum size of $6$. This is due to increased robustness when used with low-precision computation.

Image Credit: [PyTorch](https://pytorch.org/docs/master/generated/torch.nn.ReLU6.html)

ReLU6

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

UFLoss

High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss

**PIoU Loss** is a loss function for oriented object detection which is formulated to exploit both the angle and IoU for accurate oriented bounding box regression. The PIoU loss is derived from IoU metric with a pixel-wise form.

Source	High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com