Pyramid Layer Architecture Search for Accuracy-Latency Tradeoffs
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Solution Overview
Problem
Current convolutional neural network architectures for object detection are manually designed and under-optimized, particularly in their feature pyramid networks, leading to an inefficient search space and suboptimal performance.
Innovation Solution
A computing system using reinforcement learning and/or evolutionary techniques iteratively generates optimized architectures for pyramid layers, constraining cross-scale connections to combine feature representations, allowing for scalable and flexible model designs adaptable to various backbone models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual design approach is used for convolutional neural network architectures, then implementation simplicity is maintained, but optimization performance deteriorates
Solution Approach 1:
The system employs automated reinforcement learning agents that self-optimize the pyramid layer architectures without human intervention. The agents autonomously search through the design space, evaluate architectures, and iteratively improve performance metrics, allowing the system to serve itself in the optimization process rather than relying on manual design.
Solution Approach 2:
The patent replaces the manual mechanical design process with computational reinforcement learning systems. Instead of human experts manually configuring architectures, automated agents using reward-based learning algorithms explore and optimize the architecture space, substituting human cognitive processes with machine-based optimization mechanisms.
2Measurement precision
If feature pyramid network architecture is expanded to combine features at multiple scales, then detection accuracy is improved, but computational complexity grows exponentially
Solution Approach 1:
The system dynamically optimizes the pyramid layer architecture by allowing the reinforcement learning agent to adaptively configure connections and operations between feature levels. The architecture is not static but can be modified iteratively to find the optimal balance between combining multi-scale features and maintaining computational efficiency, with the agent learning which connections provide the most value.
Solution Approach 2:
The reinforcement learning approach enables systematic exploration of architectural parameters such as connection patterns, operation types, and feature level combinations. By changing these parameters iteratively based on reward signals, the system finds configurations that achieve high detection accuracy while controlling computational complexity, rather than using a fixed manually-designed architecture.
3Manufacturing precision
If exhaustive search is performed in the architecture space to find optimal configurations, then architecture optimization is improved, but search time increases exponentially
Solution Approach 1:
The reinforcement learning framework implements continuous feedback loops where the agent's architectural choices are evaluated by a reward function based on performance metrics. This feedback mechanism guides the search process, allowing the agent to learn from past decisions and avoid exploring clearly suboptimal regions of the architecture space, thereby finding good configurations much faster than exhaustive search would allow.
Solution Approach 2:
The system performs preliminary exploration of the architecture space through iterative reinforcement learning training, building up knowledge about which architectural patterns are effective. This preliminary action during the training phase enables the system to make informed decisions and converge to optimal or near-optimal architectures without requiring exhaustive search at deployment time.
Data Source
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AI summary
A computing system for producing an architecture of a pyramid layer is disclosed. The computing system can include a controller model configured to generate new architectures for a pyramid layer that receives a plurality of input feature representations output by a backbone model and, in response, outputs a plurality of output feature representations. The plurality of input feature representations can have a plurality of different input resolutions, and the plurality of output feature representations can have a plurality of different output resolutions. The computing system can be configured to perform a plurality of iterations. For each iteration, the computing system can receive a new pyramid layer architecture as an output of the controller model and evaluate one or more performance characteristics of a machine-learned pyramidal feature model that includes the backbone model and one or more pyramid layers that have the new pyramid layer architecture.