**EvoNorms** are a set of normalization-activation layers that go beyond existing design patterns. Normalization and activation are unified into a single computation graph, its structure is evolved starting from low-level primitives. EvoNorms consist of two series: B series and S series. The B series are batch-dependent and were discovered by our method without any constraint. The S series work on individual samples, and were discovered by rejecting any batch-dependent operations.

CDCC-NET is a multi-task network that analyzes the detected counter region and predicts 9 outputs: eight float numbers referring to the corner positions (x0/w, y0/h, ... , x3/w, y3/h) and an array containing two float numbers regarding the probability of the counter being legible/operational or illegible/faulty.

CDCC-NET

Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

EvoNorms

Evolving Normalization-Activation Layers

**Auxiliary Classifiers** are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. The motivation is to push useful gradients to the lower layers to make them immediately useful and improve the convergence during training by combatting the vanishing gradient problem. They are notably used in the Inception family of convolutional neural networks.

Source	Evolving Normalization-Activation Layers
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com