Knowledge Distillation Framework for Low-Label Machine Learning
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Solution Overview
Problem
Existing machine learning models require a large amount of labeled data for training, which is expensive and scarce, leading to increased computational expense and inefficiency.
Innovation Solution
A framework integrating self-supervised learning, supervised learning, and semi-supervised learning, with knowledge distillation techniques to transfer knowledge from a complex teacher model to a simpler student model, utilizing unlabeled data for pretraining and fine-tuning, and employing a duplicate removal module to enhance model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deeper neural network architectures are used to improve object detection accuracy, then prediction accuracy is improved, but computational expense at runtime increases
Solution Approach 1:
The patent creates a student network that copies the knowledge and predictions of a pre-trained teacher network. The student network is trained to mimic the teacher's outputs on unlabeled data, then fine-tuned with labeled data. This copying approach allows the system to achieve comparable accuracy to deep teacher networks while using a simpler, more computationally efficient student architecture for deployment.
2Reliability
If a large number of labeled data are used to train reliable models, then model reliability is improved, but data acquisition cost and time increase
Solution Approach 1:
The patent performs preliminary self-supervised pretraining of the teacher network on large amounts of unlabeled data before the actual supervised training. This preliminary action allows the model to learn useful representations from abundant unlabeled data, reducing the amount of expensive labeled data needed in subsequent supervised fine-tuning stages while maintaining model reliability.
Solution Approach 2:
The teacher network acts as an intermediary that processes unlabeled data and generates predictions which are then used to train the student network. This intermediary mechanism allows the student to learn from both the teacher's predictions on unlabeled data and direct supervision on labeled data, efficiently bridging the gap between abundant unlabeled data and scarce labeled data.
3Manufacturing precision
If more labeled data are acquired through human annotation, then training data quality is improved, but annotation cost increases
Solution Approach 1:
The system uses self-supervised learning where the model learns from unlabeled data itself without requiring human annotation. The teacher network is pre-trained on unlabeled data using self-supervised objectives, and the student network is trained using the teacher's predictions as supervisory signals. This self-service approach eliminates or reduces the need for expensive human annotation while maintaining training data quality.
4Device complexity
If knowledge distillation is used to compress the model, then device complexity is reduced, but training time increases
Solution Approach 1:
The patent performs self-supervised pretraining of the teacher network on unlabeled data before knowledge distillation. This preliminary action allows the teacher to learn robust representations that can be efficiently distilled to the student. By pretraining on unlabeled data first, the overall training time is distributed across stages, and the distillation process benefits from the teacher's pre-learned knowledge, making the compression more efficient.
Data Source
AI summary
Systems and methods employ knowledge distillation for efficient machine learning. Systems and methods integrate self-supervised learning, supervised learning, semi-supervised learning and active learning, each of which learning is executed in an iterative fashion. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based deep neural network. The standard dashboard is configured to output a report regarding the object information.