Neural Network Decay Parameters for Sparse Video Stream Training
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Solution Overview
Problem
Training sequential machine learning models for tasks like real-time object detection in video streams is costly due to the need for extensive labeled data, and using sparsely labeled data leads to inaccurate recursive state updates and degraded object detection accuracy.
Innovation Solution
Implementing a three-phase training process for neural networks, including initial training with small labeled sequences, fine-tuning with longer sequences, and hyper-optimizing leakage parameters to ensure accurate inference on continuous data streams using exponential decay to weight recent state information more heavily.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional training approaches are used with extensive labeled data, then training accuracy can be improved, but training costs and time consumption increase significantly
Solution Approach 1:
The training process is divided into two distinct phases: (1) initial training on sparsely labeled data to establish baseline model parameters, and (2) fine-tuning on continuously streamed data to adapt and improve accuracy over time. This segmentation allows the system to achieve good performance without requiring extensive manual labeling of all training data.
Solution Approach 2:
The system performs preliminary training using available sparsely labeled data before deployment. This preliminary action establishes a functional model that can then be continuously refined through fine-tuning on incoming data streams, eliminating the need to wait for complete labeled datasets.
2Ease of manufacture
If sparsely labeled data is used for training, then training costs are reduced, but object detection accuracy degrades due to inaccurate recursive state updates
Solution Approach 1:
The system implements continuous fine-tuning where the neural network processes incoming video frames and uses the results to update its internal state and parameters. This feedback mechanism allows the model to correct inaccuracies from sparse labeling by learning from the continuous stream of unlabeled data, thereby maintaining high detection accuracy while using minimal labeled data.
Solution Approach 2:
The fine-tuning process operates continuously on incoming video data streams, allowing the model to constantly refine its parameters and maintain accurate object detection. This continuous action compensates for the limitations of sparsely labeled training data by leveraging the temporal continuity and redundancy in video streams.
3Productivity
If sequential models are used to exploit similarity of adjacent frames, then processing efficiency is improved, but the requirement for labeled training data increases
Solution Approach 1:
The system applies sequential processing selectively - using full sequential modeling only when beneficial, while relying more on independent frame processing when labeled data is scarce. The fine-tuning phase processes continuous streams to implicitly learn temporal patterns without requiring explicit labeled sequences, thus achieving partial sequential benefits without the full data requirement.
Data Source
AI summary
Apparatuses, systems, and techniques to identify objects with in an image. In at least one embodiment, objects are identified in an image using one or more neural networks, in which the one or more neural networks are trained using one or more decay parameters.