**Gradient Checkpointing** is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.

**DAFNe** is a dense one-stage anchor-free deep model for oriented object detection. It is a deep neural network that performs predictions on a dense grid over the input image, being architecturally simpler in design, as well as easier to optimize than its two-stage counterparts. Furthermore, it reduces the prediction complexity by refraining from employing bounding box anchors. This enables a tighter fit to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. Moreover, it introduces an orientation-aware generalization of the center-ness function to arbitrary quadrilaterals that takes into account the object's orientation and that, accordingly, accurately down-weights low-quality predictions

DAFNe

DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection

Gradient Checkpointing

Training Deep Nets with Sublinear Memory Cost

**SRGAN Residual Block** is a residual block used in the [SRGAN](https://paperswithcode.com/method/srgan) generator for image super-resolution. It is similar to standard [residual blocks](https://paperswithcode.com/method/residual-block), although it uses a [PReLU](https://paperswithcode.com/method/prelu) activation function to help training (preventing sparse gradients during [GAN](https://paperswithcode.com/method/gan) training).

Source	Training Deep Nets with Sublinear Memory Cost
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com