Collaborative Learning System for Machine Representations
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Solution Overview
Problem
Current machine learning techniques struggle to determine accurate machine representations for user concepts embodied in unstructured datasets, such as video clips, images, and text, and fail to adapt to dynamically changing user concepts.
Innovation Solution
A collaborative learning system that receives a user-specified object and computes similarity scores between its feature vector and candidate objects, iteratively refining the target feature vector and similarity function based on user feedback to improve machine representations of the user's concept.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supervised machine learning techniques are used to compute machine representations, then labeled data can be processed efficiently, but the system cannot adapt to dynamically changing user concepts without retraining
Solution Approach 1:
The system implements feedback loops where user interactions with search results (clicks, dwell time, refinements) continuously inform and update the machine learning model. This allows the system to adapt to evolving user concepts in real-time without requiring complete retraining, resolving the contradiction between processing efficiency and concept adaptability.
Solution Approach 2:
The patent transforms the static supervised learning approach into a dynamic system where the model parameters and feature representations can evolve continuously based on user feedback. This dynamic adaptation enables the system to maintain both efficiency and adaptability by updating only the necessary components rather than retraining entire models.
2Ease of manufacture
If unsupervised machine learning techniques are used to learn machine representations, then the system can process unlabeled data without labels, but there is no guarantee that examples of the same concept will be clustered together
Solution Approach 1:
The patent merges unsupervised pre-training with supervised fine-tuning in a hybrid approach. The unsupervised component provides initial feature representations from unlabeled data, while the supervised component refines these representations using labeled examples to ensure accurate concept clustering. This combination resolves the contradiction by maintaining both processing ease and clustering accuracy.
Solution Approach 2:
The system performs preliminary unsupervised learning to generate initial feature representations, then applies supervised refinement in a second stage. This preliminary action allows the system to benefit from easy unlabeled data processing while subsequently achieving accurate concept clustering through targeted supervised learning.
3Reliability
If the system computes similarity scores for all candidate objects, then comprehensive search results are provided, but computational complexity and time increase
Solution Approach 1:
The patent segments the candidate object set into multiple groups or hierarchies, and computes similarity scores in a staged manner rather than evaluating all candidates simultaneously. This segmentation allows the system to maintain search completeness while reducing computational time by processing subsets of data in parallel or in priority-ordered sequences.
Solution Approach 2:
The system computes similarity scores for a partial set of most promising candidate objects first, based on initial filtering or heuristic scoring. This partial action provides sufficiently comprehensive results for most use cases while significantly reducing computation time, accepting that a small number of edge cases may require additional processing.
Data Source
AI summary
Embodiments provide a system and method for performing collaborative learning of machine representations of a concept. During operation, the system can receive a user-specified object associated with a user's concept of interest. The system can compute a similarity score between a target feature vector associated with the user-specified object and a respective feature vector for a set of candidate objects. The system can determine, based on the similarity score, a first subset of candidate objects that satisfy a similarity threshold. The system can receive, via a GUI, a first user-feedback associated with a visual representation of the first subset of candidate objects. The first user-feedback can represent an elaboration of a current user's concept of interest. The system can then modify, based on the first user-feedback, the target feature vector and the similarity function, thereby providing an improved model for machine representations of a current user's concept of interest.