**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.

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)

AutoEncoder

Reducing the Dimensionality of Data with Neural Networks

One-Shot Aggregation

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

An **Invertible Rescaling Network (IRN)** is a network for image rescaling.  According to the Nyquist-Shannon sampling theorem, high-frequency contents are lost during downscaling. Ideally, we hope to keep all lost information to perfectly recover the original HR image, but storing or transferring the high-frequency information is unacceptable. In order to well address this challenge, the Invertible Rescaling Net (IRN) captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Given an HR image $x$, IRN not only downscales it into a LR image y, but also embeds the case-specific high-frequency content into an auxiliary case-agnostic latent variable $z$, whose marginal distribution
obeys a fixed pre-specified distribution (e.g., isotropic Gaussian). Based on this model,
we use a randomly drawn sample of $z$ from the pre-specified distribution for the inverse upscaling procedure, which holds the most information that one could have in upscaling.

Source	An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com