**AlexNet** is a classic convolutional neural network architecture. It consists of convolutions, [max pooling](https://paperswithcode.com/method/max-pooling) and dense layers as the basic building blocks. Grouped convolutions are used in order to fit the model across two GPUs.

**PonderNet** is an adaptive computation method that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost and generalization.

PonderNet

PonderNet: Learning to Ponder

AlexNet

ImageNet Classification with Deep Convolutional Neural Networks

**Differentiable Augmentation (DiffAugment)** is a set of differentiable image transformations used to augment data during [GAN](https://paperswithcode.com/method/gan) training. The transformations are applied to the real and generated images. It enables the gradients to be propagated through the augmentation back to the generator, regularizes
the discriminator without manipulating the target distribution, and maintains the balance of training
dynamics. Three choices of transformation are preferred by the authors in their experiments: Translation, [CutOut](https://paperswithcode.com/method/cutout), and Color.

Source	ImageNet Classification with Deep Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com