Memory Augmented Domain Adaptation for Machine Learning
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Solution Overview
Problem
Traditional machine learning techniques face performance degradation when adapting to new domains due to diversity in training samples, particularly in controlled environments like medical imaging, and require large datasets for retraining, leading to issues like 'catastrophic forgetting' and inefficiency in domain adaptation.
Innovation Solution
A memory augmented domain adaptation system that computes features and identifies support images to augment the input to a machine-learnt model, allowing it to adapt to a new target domain using a small set of samples, thereby enhancing performance and facilitating continuous learning without modifying the base model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning techniques are trained using large datasets from source domain, then model performance on source domain is improved, but performance degrades on target domain due to domain adaptation problems
Solution Approach 1:
The system pre-computes and stores feature representations and support images from the source domain dataset before deployment. When adapting to a target domain, these pre-computed features are retrieved and used to augment the target domain samples, enabling rapid adaptation without retraining the entire model from scratch.
Solution Approach 2:
The patent introduces an intermediate memory buffer that stores feature representations and support images. This memory buffer acts as a mediator between the source domain training data and the target domain adaptation process, allowing the model to leverage source domain knowledge while adapting to target domain characteristics without direct retraining.
2Adaptability or versatility
If neural networks are retrained with new target domain samples to adapt to new domain, then adaptability to target domain is improved, but catastrophic forgetting occurs and performance on source domain degrades
Solution Approach 1:
The system pre-computes feature representations and identifies support images from the source domain before deployment. During target domain adaptation, these pre-computed features serve as a regularization constraint, preventing the model from forgetting source domain knowledge while learning target domain characteristics.
Solution Approach 2:
The patent implements a feedback mechanism where the model's predictions on target domain samples are compared against the stored source domain features and support images in the memory buffer. This feedback loop guides the adaptation process to maintain consistency with source domain knowledge while adapting to target domain variations.
3Measurement precision
If large number of target domain samples are collected for retraining, then adaptation accuracy is improved, but data acquisition time and cost increase
Solution Approach 1:
The system creates feature representations and support images as copies of the source domain data, storing them in a memory buffer. During target domain adaptation, these copied features are used to augment the limited target domain samples, effectively synthesizing additional training data without requiring extensive collection of new samples.
Solution Approach 2:
The patent transforms the source domain images into feature representations and support images with modified parameters (feature vectors, extracted characteristics) that capture essential patterns. These parameter-transformed features are then used to guide adaptation to the target domain, reducing dependency on the quantity of target domain samples.
4Measurement precision
If extensive retraining is performed to adapt to new domain, then model accuracy on target domain is improved, but computational resources and training time are consumed
Solution Approach 1:
The system extracts and stores only the essential feature representations and support images from the source domain data, separating the critical adaptive information from the complete dataset. During target domain adaptation, only these extracted features are used for augmentation and guidance, avoiding the computational burden of processing entire datasets.
Solution Approach 2:
The computationally intensive feature extraction and support image identification are performed in advance during an offline phase. During online target domain adaptation, the system only needs to retrieve and use the pre-computed features, significantly reducing the computational energy required for real-time adaptation.
Data Source
AI summary
A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.