RNN Object Inference Using Decay Parameters for Sparse Video Training
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Solution Overview
Problem
Training sequential machine learning models, such as RNNs, using sparsely labeled video data is costly and inefficient, leading to challenges in maintaining state information continuity and accuracy in real-time object detection tasks like autonomous driving.
Innovation Solution
A three-phase training approach is employed, involving initial training with small labeled sequences, fine-tuning with longer sequences, and hyper-optimization with exponential decay to manage state leakage, allowing for efficient training and accurate inference on continuous video streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If sparsely labeled video data is used for training sequential machine learning models, then training costs are reduced, but training accuracy and model performance deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training the RNN model on synthetically generated video data before fine-tuning on real sparsely labeled data. This preliminary training phase prepares the model to effectively utilize the limited real labeled data, thereby maintaining training accuracy while working with sparse labels. The synthetic data generation creates realistic training scenarios that bridge the gap between having no labels and having sufficient labeled data.
Solution Approach 2:
The patent introduces synthetic video data as an intermediary between the availability of sparsely labeled real data and the requirement for comprehensive training data. This intermediary synthetic dataset, generated through simulation environments, allows the model to learn general patterns and behaviors before adapting to real-world data, thus improving training accuracy without requiring extensive real labeled data.
2Measurement precision
If state information is maintained across all frames in continuous video streams, then detection accuracy is improved, but memory requirements and computational complexity increase
Solution Approach 1:
The patent applies local quality by selectively maintaining state information based on its relevance to current detection tasks. Instead of uniformly storing state for all frames, the system adapts the state retention policy locally - maintaining detailed state information for critical frames and objects while reducing or eliminating state storage for less important frames. This selective approach preserves detection accuracy for critical events while reducing overall memory consumption.
Solution Approach 2:
The patent changes the parameter of state information retention by introducing a decay mechanism where the influence of historical state information gradually decreases over time. This parameter change allows the model to weigh recent state information more heavily while still benefiting from historical context, thereby maintaining detection accuracy for recent events while reducing the memory burden of storing and processing equally weighted historical states.
3Reliability
If exponential decay is applied to manage state leakage during inference, then state information relevance is maintained, but model complexity increases
Solution Approach 1:
The patent implements periodic action through the exponential decay mechanism that systematically reduces the weight of historical state information at regular computational intervals during inference. This periodic attenuation of state influence ensures that the model periodically resets its focus to current observations while retaining beneficial historical context, thereby maintaining state information relevance without requiring complex architectural modifications. The decay is applied as a simple multiplicative factor in the state update equation.
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.