Batch-Normalized Recurrent Highway Networks for Gradient Stability
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Solution Overview
Problem
Deep learning models, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), face challenges with gradient stability issues such as vanishing and exploding gradients, which hinder training and performance in tasks like object recognition and image captioning, especially as network depth increases.
Innovation Solution
The implementation of batch normalization techniques and Recurrent Highway Networks (RHNs) with relaxed constraints, specifically normalizing transition units in highway layers, addresses gradient stability issues by controlling gradient flow and enhancing network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the depth of Recurrent Neural Networks (RNNs) is increased to reduce error on competitive benchmark testing, then the network's ability to model complex sequences is improved, but gradient stability deteriorates causing vanishing or exploding gradients over time
Solution Approach 1:
The patent introduces batch normalization as an intermediary mechanism between layers in the RNN. This normalization layer acts as a mediator that stabilizes the gradient flow by normalizing the inputs to each layer, preventing both vanishing and exploding gradients while allowing the network to achieve greater depth for improved benchmark performance
Solution Approach 2:
The patent modifies the network architecture by incorporating batch normalization layers that dynamically adjust parameters (gamma and beta) to scale and shift the normalized inputs. This parameter transformation allows the network to maintain stable gradients across increased depth while preserving the ability to learn complex temporal patterns
2Productivity
If batch normalization is applied to RNNs to reduce covariate shifts between time steps, then training convergence is accelerated, but generalization performance on sequence modeling tasks does not improve
Solution Approach 1:
The patent employs dynamic batch normalization where the normalization statistics are computed differently for training and inference phases. During training, batch statistics are used to normalize inputs and accelerate convergence, while during inference, running averages of statistics maintain stable generalization performance across sequence modeling tasks
3Reliability
If highway layers with transform gate and carry gate are used to control gradient flow, then vanishing and exploding gradient problems are avoided, but the network constraints limit the power of the network
Solution Approach 1:
The patent integrates batch normalization into the highway network architecture, creating a multi-functional system where the normalization layer serves both to stabilize gradients (reliability) and to provide additional transformable features that enhance network capacity (versatility). This universal approach allows the network to maintain stability while gaining additional adaptive power
Data Source
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AI summary
Embodiments of the present disclosure relate to a recurrent framework based on Recurrent Highway Networks (RHNs) for sequence modeling using batch normalization. In certain embodiments, constraints within the RHNs are relaxed to reduce or avoid gradient vanishing or exploding by normalizing the current transition units in highway layers.