**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and an inverted residual structure (see components).

**Network Dissection** is an interpretability method for [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks) that evaluates the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. 

The measurement of interpretability proceeds in three steps:

- Identify a broad set of human-labeled visual concepts.
- Gather the response of the hidden variables to known concepts.
- Quantify alignment of hidden variable−concept pairs.

Network Dissection

Interpreting Deep Visual Representations via Network Dissection

FBNet

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

**DouZero** is an AI system for the card game DouDizhu that enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors. The [Q-network](https://paperswithcode.com/method/dqn) of DouZero consists of an [LSTM](https://paperswithcode.com/method/lstm) to encode historical actions and six layers of [MLP](https://paperswithcode.com/method/feedforward-network) with hidden dimension of 512. The network predicts a value for a given state-action pair based on the concatenated representation of action and state.

Source	FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com