A **Style-based Recalibration Module (SRM)** is a module for convolutional neural networks that adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight via channel-independent style integration. By incorporating the relative importance of individual styles into feature maps, SRM is aimed at enhancing the representational ability of a CNN.

The overall structure of SRM is illustrated in the Figure to the right. It consists of two main components: style pooling and style integration. The style pooling operator extracts style features
from each channel by summarizing feature responses across spatial dimensions. It is followed by the style integration operator, which produces example-specific style weights by utilizing the style features via channel-wise operation. The style weights finally recalibrate the feature maps to either
emphasize or suppress their information.

A **Dynamic Memory Network** is a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. 

The DMN consists of a number of modules:

- Input Module: The input module encodes raw text inputs from the task into distributed vector representations. The input takes forms like a sentence, a long story, a movie review and so on.
- Question Module: The question module encodes the question of the task into a distributed
vector representation. For question answering, the question may be a sentence such as "Where did the author first fly?". The representation is fed into the episodic memory module, and forms the basis, or initial state, upon which the episodic memory module iterates.
- Episodic Memory Module: Given a collection of input representations, the episodic memory module chooses which parts of the inputs to focus on through the attention mechanism. It then produces a ”memory” vector representation taking into account the question as well as the previous memory. Each iteration provides the module with newly relevant information about the input. In other words,
the module has the ability to retrieve new information, in the form of input representations, which were thought to be irrelevant in previous iterations.
- Answer Module: The answer module generates an answer from the final memory vector of the memory module.

Dynamic Memory Network

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Style-based Recalibration Module

SRM : A Style-based Recalibration Module for Convolutional Neural Networks

An effective approach to the quantification of patchiness in biomedical images according to their local edge densities.

Source	SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com