**DenseNet-Elastic** is a convolutional neural network that is a modification of a [DenseNet](https://paperswithcode.com/method/densenet) with elastic blocks (extra upsampling and downsampling).

**Mixup** is a data augmentation technique that generates a weighted combination of random image pairs from the training data. Given two images and their ground truth labels: $\left(x\_{i}, y\_{i}\right), \left(x\_{j}, y\_{j}\right)$, a synthetic training example $\left(\hat{x}, \hat{y}\right)$ is generated as:

$$ \hat{x} = \lambda{x\_{i}} + \left(1 − \lambda\right){x\_{j}} $$
$$ \hat{y} = \lambda{y\_{i}} + \left(1 − \lambda\right){y\_{j}} $$

where $\lambda \sim \text{Beta}\left(\alpha = 0.2\right)$ is independently sampled for each augmented example.

Mixup

mixup: Beyond Empirical Risk Minimization

DenseNet-Elastic

ELASTIC: Improving CNNs with Dynamic Scaling Policies

**IoU-Net** is an object detection architecture that introduces localization confidence. IoU-Net learns to predict the IoU between each detected bounding box and the matched ground-truth. The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes. Furthermore, an optimization-based bounding box refinement method is proposed, where the predicted IoU is formulated as the objective.

Source	ELASTIC: Improving CNNs with Dynamic Scaling Policies
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com