Things to Know About the Feature Pyramid Network (FPN) Design
The Feature Pyramid Network (FPN) design is a novel approach to neural architecture search and feature fusing. FPN fuses multi-scale features at levels 3 to 7 (P3-P7) via a top-down method, which reduces the number of permutations required for each individual feature.
The Feature Pyramid Network is a general framework for learning the hierarchical representation of images. It consists of three layers at level 0, 1, and 2 (L0-L2) where each layer has differentiable functions that can be applied to the input vectors based on their corresponding scale levels.
The first layer consists of only one neuron that outputs an activation value indicating whether it has found an edge or not; this is called L1–1. Next, there are two neurons in L2 with inputs coming from both sides: if one side indicates an edge then it will be output as 1 while its opposite side indicates no edge or even negative values so this means that these neurons are actually detecting edges between two segments within an image such as lines or edges but also structures like curves which might exist within objects such as faces! Each neuron, therefore, represents one aspect of what makes up our perception system — our eyesight ability to detect shapes, etc., through their ability to combine signals from many different scales simultaneously across multiple channels into one final result (e.g., color).
On top of FPN, PANet provides a second bottom-up route. This means that if you train the neural network on a given dataset and then use it to predict an unknown label, it will find the best feature set (and therefore provide better results than just using FPN). PANet can be used with any encoder or decoder — it doesn’t matter whether you’re using SVMs or RNNs as long as they’re trained on your data and then used to make predictions in future episodes!
NAS-FPN is a feature pyramid network (FPN) with a neural architecture search component applied at every time step in order for it to be able to adapt its weights based on new information about what kind of data it has been presented with so far during training and testing phases respectively. NAS-FPN continuously applies the same block after using neural architecture search to identify an irregular feature network topology. The process of identifying this irregular feature network topology is referred to as neural architecture search, which uses a three-layer feedforward network structure and performs gradient descent optimization on each layer. The feedforward neural network has two hidden layers with one output per layer, but each hidden layer has four neurons instead of five due to regularization (reduction in variance).
BiFPN is a combination of NAS-FPN and PANet, where the first step is to fuse features from different levels into a single network. The second step adds shortcut fusing and similarity based routing (SBFR), which will be described below. BiFPN uses NAS-FPN to fuse features from different levels into one block in order to create shortcuts between them. Then it applies SBFR to this generated structure in order to find similar paths between blocks that might not have been discovered by other means (e.g., by taking advantage of some commonalities). Finally, if there are any remaining unconnected nodes or edges after applying all these processes then they will be reattached using local-link detection techniques like GRASP
The best approach appears to be the BiFPN, which is slightly more complex than PANet but can be used with any encoder while achieving better results.
The feature network design approach is quite promising, but there are still many open questions to be answered: how can we create networks that support deep learning applications? how can we enhance the underlying data processing techniques? how do we make these networks more scalable and efficient? Can they be used in applications such as audio transcoding or speech recognition? These are all important questions that will help us understand where FPN will fit into our world tomorrow.
There are additional FPN designs that are more complex. Yolo-ReT suggested a fresh layout.
