**RotNet** is a self-supervision approach that relies on predicting image rotations as the pretext task
in order to learn image representations.

**Elastic Dense Block** is a skip connection block that modifies the [Dense Block](https://paperswithcode.com/method/dense-block) with downsamplings and upsamplings in parallel branches at each layer to let the network learn from a data scaling policy in which inputs are processed at different resolutions in each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.

Elastic Dense Block

ELASTIC: Improving CNNs with Dynamic Scaling Policies

RotNet

RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks

**Class-MLP** is an alternative to [average pooling](https://paperswithcode.com/method/average-pooling), which is an adaptation of the class-attention token introduced in [CaiT](https://paperswithcode.com/method/cait). In CaiT, this consists of two layers that have the same structure as the [transformer](https://paperswithcode.com/method/transformer), but in which only the class token is updated based on the frozen patch embeddings. In Class-MLP, the same approach is used, but after aggregating the patches with a [linear layer](https://paperswithcode.com/method/linear-layer), we replace the [attention-based interaction](https://paperswithcode.com/method/scaled) between the class and patch embeddings by simple linear layers, still keeping the patch embeddings frozen. This increases the performance, at the expense of adding some parameters and computational cost. This pooling variant is referred to as “class-MLP”, since the purpose of these few layers is to replace average pooling.

Source	RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com