An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and then performs a reconstruction of the input with this latent code (the decoder).

Image: [Michael Massi](https://en.wikipedia.org/wiki/Autoencoder#/media/File:Autoencoder_schema.png)

A **Feature Pyramid Network**, or **FPN**, is a feature extractor that takes a single-scale image of an arbitrary size as input, and outputs proportionally sized feature maps at multiple levels, in a fully convolutional fashion. This process is independent of the backbone convolutional architectures. It therefore acts as a generic solution for building feature pyramids inside deep convolutional networks to be used in tasks like object detection.

The construction of the pyramid involves a bottom-up pathway and a top-down pathway.

The bottom-up pathway is the feedforward computation of the backbone ConvNet, which computes a feature hierarchy consisting of feature maps at several scales with a scaling step of 2. For the feature
pyramid, one pyramid level is defined for each stage. The output of the last layer of each stage is used as a reference set of feature maps. For [ResNets](https://paperswithcode.com/method/resnet) we use the feature activations output by each stage’s last [residual block](https://paperswithcode.com/method/residual-block). 

The top-down pathway hallucinates higher resolution features by upsampling spatially coarser, but semantically stronger, feature maps from higher pyramid levels. These features are then enhanced with features from the bottom-up pathway via lateral connections. Each lateral connection merges feature maps of the same spatial size from the bottom-up pathway and the top-down pathway. The bottom-up feature map is of lower-level semantics, but its activations are more accurately localized as it was subsampled fewer times.

Feature Pyramid Networks for Object Detection

AutoEncoder

Reducing the Dimensionality of Data with Neural Networks

**One-Shot Aggregation** is an image model block that is an alternative to [Dense Blocks](https://paperswithcode.com/method/dense-block), by aggregating intermediate features. It is proposed as part of the [VoVNet](https://paperswithcode.com/method/vovnet) architecture. Each [convolution](https://paperswithcode.com/method/convolution) layer is connected by two-way connection. One way is connected to the subsequent layer to produce the feature with a larger receptive field while the other way is aggregated only once into the final output feature map. The difference with [DenseNet](https://paperswithcode.com/method/densenet) is that the output of each layer is not routed to all subsequent intermediate layers which makes the input size of intermediate layers constant.

Source	Reducing the Dimensionality of Data with Neural Networks
Year	2000
Data Source	Papers with Code archive (CC BY-SA)