**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.

Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout can also mitigate underfitting when used at the start of training. During the early phase, we find dropout reduces the directional variance of gradients across mini-batches and helps align the mini-batch gradients with the entire dataset's gradient. This helps counteract the stochasticity of SGD and limit the influence of individual batches on model training. Our findings lead us to a solution for improving performance in underfitting models - early dropout: dropout is applied only during the initial phases of training, and turned off afterwards. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. Additionally, we explore a symmetric technique for regularizing overfitting models - late dropout, where dropout is not used in the early iterations and is only activated later in training. Experiments on ImageNet and various vision tasks demonstrate that our methods consistently improve generalization accuracy. Our results encourage more research on understanding regularization in deep learning and our methods can be useful tools for future neural network training, especially in the era of large data. Code is available at https://github.com/facebookresearch/dropout .

Early Dropout

Dropout Reduces Underfitting

Network Dissection

Interpreting Deep Visual Representations via Network Dissection

**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).

Source	Interpreting Deep Visual Representations via Network Dissection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com