**Inception-ResNet-v2 Reduction-B** is an image model block used in the [Inception-ResNet-v2](https://paperswithcode.com/method/inception-resnet-v2) architecture.

In recent years, there has been a growing interest on deep learning-based pansharpening.
Research has mainly focused on architectures.
However, lacking a ground truth, model training is also a major issue.
A popular approach is to train networks in a reduced resolution domain, using the original data as ground truths.
The trained networks are then used on full resolution data, relying on an implicit scale invariance hypothesis.
Results are generally good at reduced resolution, but more questionable at full resolution.

Here, we propose a full-resolution training framework for deep learning-based pansharpening.
Training takes place in the high resolution domain, relying only on the original data, with no loss of information.
To ensure spectral and spatial fidelity, suitable losses are defined,
which force the pansharpened output to be consistent with the available panchromatic and multispectral input.
Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework
guarantee an excellent performance in terms of both full-resolution numerical indexes and visual quality.
The framework is fully general, and can be used to train and fine-tune any deep learning-based pansharpening network.

Z-PNN

Pansharpening by convolutional neural networks in the full resolution framework

Inception-ResNet-v2 Reduction-B

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks. 

Formally, denoting the desired underlying mapping as $\mathcal{H}(x)$, we let the stacked nonlinear layers fit another mapping of $\mathcal{F}(x):=\mathcal{H}(x)-x$. The original mapping is recast into $\mathcal{F}(x)+x$.

There is empirical evidence that these types of network are easier to optimize, and can gain accuracy from considerably increased depth.

Source	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com