On-the-fly Model Adaptation for Privacy-Compliant Image Processing
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Solution Overview
Problem
Existing machine-learning methods for image processing are data-hungry and require manual annotation, which is costly and often not feasible due to privacy concerns, and existing solutions like JIT-Net are limited to specific tasks and not applicable for on-the-fly adaptation in real-world scenarios, especially on devices with limited resources.
Innovation Solution
A method for on-the-fly adaptation of predictive models using a first predictive model to update a second model based on context-based images from an optical sensor, where each image is used only once, and the parameters of the second model are updated using a cost function computed from the predictions of both models, enabling efficient hyper-specification and inference on devices with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on large datasets to improve accuracy, then model performance is improved, but data storage requirements and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training a teacher model on large datasets offline, then uses this pre-trained model to generate pseudo-labels for online adaptation. This eliminates the need to store large amounts of training data during deployment, as the teacher model's knowledge is transferred through its predictions rather than raw data
Solution Approach 2:
The system creates a copy of the knowledge from the teacher model by generating pseudo-labels through its predictions. These pseudo-labels serve as distilled knowledge that can guide the student model's adaptation without requiring access to the original large training datasets, effectively copying essential information in a compressed form
2Measurement precision
If manual annotation of real-world data is performed to improve model adaptation, then model performance on specific tasks is improved, but cost and time requirements increase
Solution Approach 1:
The system implements self-service by enabling the teacher model to automatically generate pseudo-labels for the student model's training data. This eliminates the need for manual human annotation, as the system serves itself by creating training labels through the teacher model's predictions, significantly reducing both time and cost
Solution Approach 2:
The teacher model acts as an intermediary between the available unlabeled data and the student model's training requirements. Instead of requiring direct manual annotation, the teacher model mediates by generating pseudo-labels that bridge the gap, enabling automated adaptation without human intervention
3Measurement precision
If existing solutions like JIT-Net are used for model adaptation, then instance segmentation performance is improved, but applicability to other tasks such as object detection and classification is limited
Solution Approach 1:
The system achieves universality by designing a task-agnostic online adaptation framework that can be applied to multiple computer vision tasks including object detection, classification, and instance segmentation. The core mechanism of using a teacher model to generate pseudo-labels for student model adaptation is task-independent, allowing the same approach to work across different prediction tasks
4Productivity
If images are used multiple times for training the student model, then training efficiency is improved, but privacy compliance and data freshness are compromised
Solution Approach 1:
The system applies discarding and recovering by processing each image only once through the teacher model to generate pseudo-labels, then discarding the original image data. The essential information is recovered in the form of pseudo-labels that are sufficient for student model training, eliminating the need to retain or reprocess the original images and thus ensuring privacy compliance
Data Source
AI summary
The present invention is notably directed to methods of machine-learning for an on-the-fly adaptation of a predictive model configured for image processing. The method comprises providing a first predictive model having been configured to provide at least one first prediction task, providing a second predictive model having been configured to provide a second prediction task, the second predictive model comprising one or more parameters, the second prediction task being derivable from the first prediction task, providing context-based images from a first stream of images obtained from an optical sensor to both the first and the second predictive models, each provided image being provided just once on-the-fly, and performing an on-the-fly adaptation for the second predictive model, the on-the-fly adaptation comprising, for each provided image: performing a respective first prediction by the first predictive model and a respective second prediction by the second predictive model, computing a cost function of the respective first prediction and the respective second prediction, and updating the one or more parameters of the second predictive model based on the computed cost function.