**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.

**PGNet** is a point-gathering network for reading arbitrarily-shaped text in real-time. It is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency. Additionally, reasoning the relations between each character and its neighbors, a graph refinement module (GRM) is proposed to optimize the coarse recognition and improve the end-to-end performance.

PGNet

PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network

Elastic Dense Block

ELASTIC: Improving CNNs with Dynamic Scaling Policies

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

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