Balanced Concatenation Layer for Neural Network Training
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Solution Overview
Problem
Deep neural networks face challenges in training dynamics due to uncontrollable increases in weight magnitudes and imbalances in contributions from different paths, leading to inaccurate models and increased computational and financial costs.
Innovation Solution
Implementing a balanced concatenation layer that normalizes and scales inputs from different paths, decoupling their contributions from channel counts, allowing independent hyperparameter adjustment for enhanced flexibility and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard concatenation layers are used to combine inputs from different paths, then the neural network can process multiple data streams, but the contributions from different paths become imbalanced due to channel count differences, leading to uncontrollable weight magnitude increases and reduced model accuracy
Solution Approach 1:
The patent applies local quality by making different parts of the concatenation operation have different properties. Specifically, it normalizes the output of each path individually based on its channel count before concatenation, ensuring that each path contributes equally to the final representation regardless of its original channel dimension. This localized normalization prevents any single path from dominating due to having more channels.
Solution Approach 2:
The patent changes the parameter scaling in the concatenation operation by introducing path-specific normalization factors. Instead of treating all channels equally, it adjusts the scale of each path's contribution based on its channel count, thereby controlling the magnitude of weights and preventing imbalance. This parameter adjustment ensures that paths with different channel counts contribute equally to the concatenated output.
2Device complexity
If paths with different channel counts are concatenated directly, then the network architecture remains simple, but the contributions from different paths are imbalanced, causing training dynamics to deteriorate and computational costs to increase
Solution Approach 1:
The patent applies preliminary action by performing normalization on each path's output before the concatenation operation. This pre-processing step adjusts the scale of each path based on its channel count, ensuring that when the paths are concatenated, they contribute equally. This preliminary scaling prevents training dynamics from deteriorating and avoids the need for complex post-processing corrections.
3Loss of energy
If no normalization is applied in concatenation layers, then the computational cost remains low, but the weight magnitudes increase uncontrollably and the contributions from different paths become imbalanced
Solution Approach 1:
The patent applies self-service by having each path normalize its own output based on its channel count before concatenation. This self-normalization mechanism allows each path to independently control its contribution scale without requiring external intervention or complex joint optimization. The operation is computationally efficient while effectively stabilizing weight magnitudes and preventing imbalance.
Data Source
AI summary
Apparatuses, systems, and techniques to train neural networks and to use neural networks to perform inference. In at least one embodiment, a balanced concatenation layer performs a balanced concatenation operation during a forward pass of a training iteration during the training of a neural network. In at least one embodiment, a balanced concatenation layer performs a balanced concatenation operation during the use of a neural network to perform inference.