F2DNet, a novel two-stage object detection architecture which eliminates redundancy of classical two-stage detectors by replacing the region proposal network with focal detection network and
bounding box head with fast suppression head.

**Mixture Normalization** is normalization technique that relies on an approximation of the probability density function of the internal representations. Any continuous distribution can be approximated with arbitrary precision using a Gaussian Mixture Model (GMM). Hence, instead of computing one set of statistical measures from the entire population (of instances in the mini-batch) as [Batch Normalization](https://paperswithcode.com/method/batch-normalization) does, Mixture Normalization works on sub-populations which can be identified by disentangling modes of the distribution, estimated via GMM. 

While BN can only scale and/or shift the whole underlying probability density function, mixture normalization operates like a soft piecewise normalizing transform, capable of completely re-structuring the data distribution by independently scaling and/or shifting individual modes of distribution.

Mixture Normalization

Training Faster by Separating Modes of Variation in Batch-normalized Models

F2DNet

F2DNet: Fast Focal Detection Network for Pedestrian Detection

Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next.

Source	F2DNet: Fast Focal Detection Network for Pedestrian Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com