Neural Network First-Layer Training to Reduce Feature Map Dependency
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Solution Overview
Problem
Existing neural networks in safety-critical applications, such as autonomous vehicles, are susceptible to overfitting due to a large number of parameters, leading to potential misclassifications that can cause severe safety issues, and require time-consuming manual labeling for supervised training.
Innovation Solution
A method for training neural networks with unsupervised regularization using a first layer loss value to adapt parameters without backpropagation, allowing unsupervised learning from unlabeled data and reducing feature map dependencies, followed by classification loss training for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training with large number of parameters is used, then classification performance can be improved, but overfitting occurs and reliability deteriorates
Solution Approach 1:
The patent applies preliminary unsupervised pre-training to initialize network parameters before supervised fine-tuning. The first layer is pre-trained using unsupervised objectives (reconstruction loss, regularization loss) to learn useful feature representations without labeled data, which provides a better starting point for subsequent supervised training and reduces overfitting risk
Solution Approach 2:
The patent dynamically adjusts the regularization strength parameter (lambda) during training and introduces dropout mechanisms that randomly zero out parameters. These parameter changes prevent the model from relying too heavily on specific parameters, thereby reducing overfitting while maintaining classification accuracy
2Measurement precision
If supervised training with manual labeling is used, then classification accuracy is improved, but training time and operational complexity increase
Solution Approach 1:
The patent performs unsupervised pre-training as a preliminary step before supervised fine-tuning. This pre-training phase uses unlabeled data to initialize the first layer, reducing the amount of labeled data needed and shortening the overall training time while maintaining or improving final accuracy
Solution Approach 2:
The training process is segmented into distinct phases: unsupervised pre-training of the first layer, then supervised fine-tuning of subsequent layers. This segmentation allows efficient use of unlabeled data in the first phase and focused optimization in the second phase, reducing total training time
3Measurement precision
If supervised training with manual labeling is used, then classification accuracy is improved, but computational energy consumption increases
Solution Approach 1:
The unsupervised pre-training phase processes unlabeled data to initialize the first layer, which reduces the computational burden during the subsequent supervised fine-tuning phase. This preliminary action distributes computational workload efficiently, reducing total energy consumption
Solution Approach 2:
The patent applies dropout mechanisms that randomly deactivate a portion of neurons during training, and uses early stopping that terminates training before full convergence. These partial actions reduce unnecessary computational operations and energy consumption while maintaining adequate classification accuracy
Data Source
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AI summary
A method for training a neural network (60), wherein the neural network (60) comprises a first layer (L1), wherein the first layer (L1) comprises a plurality of filters to provide a first layer output (z) comprising a plurality of feature maps, further wherein training of the classifier (60) comprises the steps of: • Receiving (901), by a preceding layer, a first layer input (i) in the first layer (L1), wherein the first layer input (i) is based on the input signal (x); • Determining (902) the first layer output (z) based on the first layer input (i) and a plurality of parameters (w) of the first layer (L1); • Determining (903) a first layer loss value (l) based on the first layer output (z), wherein the first layer loss value (l) characterizes a degree of dependency between the feature maps of the first layer output (z), further wherein the first layer loss value (l) is obtained in an unsupervised fashion; • Training (904) the neural network (60), wherein training comprises an adaption of the parameters (w) of the first layer (L1), wherein the adaption is based on the first layer loss value (I).