A **Wide Residual Block** is a type of [residual block](https://paperswithcode.com/method/residual-block) that utilises two conv 3x3 layers (with [dropout](https://paperswithcode.com/method/dropout)). This is wider than other variants of residual blocks (for instance [bottleneck residual blocks](https://paperswithcode.com/method/bottleneck-residual-block)). It was proposed as part of the [WideResNet](https://paperswithcode.com/method/wideresnet) CNN architecture.

**Receptive Field Block (RFB)** is a module for strengthening the deep features learned from lightweight CNN models so that they can contribute to fast and accurate detectors. Specifically, RFB makes use of multi-branch pooling with varying kernels corresponding to RFs of different sizes, applies [dilated convolution](https://paperswithcode.com/method/dilated-convolution) layers to control their eccentricities, and reshapes them to generate
final representation.

Receptive Field Block Net for Accurate and Fast Object Detection

Wide Residual Block

Wide Residual Networks

**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).

The idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.

Source	Wide Residual Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com