Encoding-Decoding Network Loss Layer Segmentation
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Solution Overview
Problem
During the backpropagation process in CNNs for image segmentation, the loss values become smaller and smaller, making it difficult to adjust the parameters of each filter effectively, leading to suboptimal segmentation results.
Innovation Solution
The implementation of an encoding-decoding network with multiple encoding filters, corresponding decoding filters, and intermediate dilation convolution filters, along with multiple loss layers that interact with the decoding filters, allows for the computation and propagation of individual losses to each filter, ensuring accurate parameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single loss layer is used in the decoding network, then the device complexity is reduced, but the loss values become too small during backpropagation to effectively adjust filter parameters
Solution Approach 1:
The patent divides the single loss computation into multiple separate loss layers, each computing loss independently at different stages of the decoding network. This segmentation allows each loss to be propagated separately through backpropagation, preventing the cumulative diminishment that occurs with a single loss layer and enabling effective parameter adjustment at each stage.
Solution Approach 2:
The patent introduces an additional dimension to the loss computation by adding multiple loss layers along the network depth dimension. Instead of computing a single loss at the final output, losses are computed at multiple intermediate stages, creating a multi-layered loss structure that preserves gradient magnitudes throughout the network.
2Manufacturing precision
If multiple loss layers are added to compute individual losses for each filter, then the parameter optimization accuracy is improved, but the device complexity increases
Solution Approach 1:
Each loss layer in the patent serves multiple functions: it computes the loss for its corresponding decoding filter, provides a gradient signal for backpropagation, and enables independent parameter optimization for that filter stage. This multi-functionality justifies the added complexity by delivering comprehensive parameter optimization benefits throughout the network.
3Productivity
If convolution operations are applied multiple times to reduce feature map size, then the computation efficiency is improved, but the loss values become too small for effective learning
Solution Approach 1:
The patent performs preliminary loss computation at intermediate stages before the final decoding output. By computing losses earlier in the decoding process when feature maps are still relatively large and gradient magnitudes are stronger, the system captures useful gradient signals before they diminish through multiple subsequent convolution operations.
Data Source
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AI summary
A method for improving image segmentation by using a learning device is disclosed. The method includes steps of: (a) if a training image is obtained, acquiring (2- K)th to (2-1)th feature maps through an encoding layer and a decoding layer, and acquiring 1st to Hth losses from the 1st to the Hth loss layers respectively corresponding to H feature maps, obtained from the H filters, among the (2-K)th to the (2-1)th feature maps; and (b) upon performing a backpropagation process, performing processes of allowing the (2-M)th filter to apply a convolution operation to (M-1)2-th adjusted feature map relayed from the (2-(M-1))th filter to obtain M1-th temporary feature map; relaying, to the (2-(M+1))th filter, M2-th adjusted feature map obtained by computing the Mth loss with the M1-th temporary feature map; and adjusting at least part of parameters of the (1-1)th to the (1-K)th filters and the (2-K)th to the (2-1)th filters.