A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.

Image Credit: [http://deeplearning.ai](http://deeplearning.ai)

**Confidence Calibration with an Auxiliary Class**, or **CCAC**, is a post-hoc confidence calibration method for DNN classifiers on OOD datasets. The key feature of CCAC is an auxiliary class in the calibration model which separates mis-classified samples from correctly classified ones, thus effectively mitigating the target DNN’s being confidently wrong. It also reduces the number of free parameters in CCAC to reduce free parameters and facilitate transfer to a new unseen dataset.

CCAC

Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets

1x1 Convolution

Network In Network

**RandomRotate** is a type of image data augmentation where we randomly rotate the image by a degree.

Source	Network In Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com