Inverted Neural Network for Synthetic Training Data Generation
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Solution Overview
Problem
Generating training data for neural networks using pre-trained models is computationally intensive and inefficient, particularly when accessing natural images with different distributions than those used to train the teacher network.
Innovation Solution
An inverted neural network is generated from a pre-trained teacher network to create synthetic training data, allowing for knowledge transfer and distillation without requiring access to natural images, using techniques like cycle consistency loss and weight transposition to improve computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained neural network is used to generate training data, then training data can be generated without accessing natural images, but computational resources are significantly consumed
Solution Approach 1:
The patent inverts the traditional training data generation approach by using a pre-trained teacher network to generate training data through its forward pass, rather than using a student network to learn from natural images. The student network is trained using the generated data without requiring access to the original natural images, thus reducing computational resource requirements while maintaining adaptability
Solution Approach 2:
The teacher network is pre-trained on natural images before being used to generate training data. This preliminary training action allows the teacher network to capture the distribution of natural images, which is then used to generate synthetic training data that the student network can learn from without direct access to natural images
2Adaptability or versatility
If natural images with different distributions are accessed for training, then training data diversity is improved, but the complexity of matching distributions increases
Solution Approach 1:
The patent introduces a teacher network as an intermediary between natural images and the student network. The teacher network processes natural images and generates synthetic training data that captures the essential distribution characteristics, serving as a mediator that simplifies the distribution matching process and reduces the complexity of directly accessing and matching natural image distributions
3Productivity
If computational resources are reduced for training, then efficiency is improved, but training accuracy may deteriorate
Solution Approach 1:
The patent creates a copy of the training process through the teacher network, which generates synthetic training data that replicates the distribution and characteristics of natural images. This copying approach allows the student network to be trained efficiently using generated data while maintaining training accuracy, as the synthetic data preserves the essential features needed for accurate learning
Data Source
AI summary
Apparatuses, systems, and techniques to invert a neural network. In at least one embodiment, one or more neural network layers are inverted and, in at least one embodiment, loaded in reverse order.