**Inception-v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an auxiliary classifer to propagate label information lower down the network (along with the use of [batch normalization](https://paperswithcode.com/method/batch-normalization) for layers in the sidehead).

**Mixture of Logistic Distributions (MoL)** is a type of output function, and an alternative to a [softmax](https://paperswithcode.com/method/softmax) layer. Discretized logistic mixture likelihood is used in [PixelCNN](https://paperswithcode.com/method/pixelcnn)++ and [WaveNet](https://paperswithcode.com/method/wavenet) to predict discrete values.

Image Credit: [Hao Gao](https://medium.com/@smallfishbigsea/an-explanation-of-discretized-logistic-mixture-likelihood-bdfe531751f0)

Mixture of Logistic Distributions

Inception-v3

Rethinking the Inception Architecture for Computer Vision

**GreedyNAS-B** is a convolutional neural network discovered using the [GreedyNAS](https://paperswithcode.com/method/greedynas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building blocks used are inverted residual blocks (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)) and squeeze-and-excitation blocks.

Source	Rethinking the Inception Architecture for Computer Vision
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com