**Good Feature Matching** is an active map-to-frame feature matching method. Feature matching effort is tied to submatrix selection, which has combinatorial time complexity and requires choosing a scoring metric. Via simulation, the Max-logDet matrix revealing metric is shown to perform best.

A **Focal Loss** function addresses class imbalance during training in tasks like object detection. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard misclassified examples. It is a dynamically scaled cross entropy loss, where the scaling factor decays to zero as confidence in the correct class increases. Intuitively, this scaling factor can automatically down-weight the contribution of easy examples during training and rapidly focus the model on hard examples. 

Formally, the Focal Loss adds a factor $(1 - p\_{t})^\gamma$ to the standard cross entropy criterion. Setting $\gamma>0$ reduces the relative loss for well-classified examples ($p\_{t}>.5$), putting more focus on hard, misclassified examples. Here there is tunable *focusing* parameter $\gamma \ge 0$. 

$$ {\text{FL}(p\_{t}) = - (1 - p\_{t})^\gamma \log\left(p\_{t}\right)} $$

Focal Loss

Focal Loss for Dense Object Detection

Good Feature Matching

Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency

**Singular Value Clipping (SVC)** is an adversarial training technique used by [TGAN](https://paperswithcode.com/method/tgan) to enforce the 1-Lipschitz constraint of the [WGAN](https://paperswithcode.com/method/wgan) objective. It is a constraint to all linear layers in the discriminator that satisfies the spectral norm of weight parameter $W$ is equal or less than one. This
means that the singular values of weight matrix are all one or less. Therefore singular value decomposition (SVD) is performed after a parameter update, replacing all the singular values larger than one with one, and the parameters are reconstructed with them. The same operation is applied to convolutional layers by interpreting a higher order tensor in weight parameter as a matrix $\hat{W}$.

Source	Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
Year	2000
Data Source	Papers with Code archive (CC BY-SA)