Multiscale Deep Equilibrium Model Memory Efficiency
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Solution Overview
Problem
Deep Equilibrium (DEQ) models face challenges in defining a function with a fixed point, root-finding procedures, and backward passes, particularly in memory efficiency and hierarchical learning for computer vision tasks, as they maintain the same resolution and do not explicitly define multiple layers, making it difficult to downsample at intermediate points.
Innovation Solution
The Multiscale Deep Equilibrium (MDEQ) model addresses these challenges by accepting input at multiple resolutions, learning in a hierarchical fashion, and using an equilibrium solver to fuse feature tensors across resolutions through upsampling or downsampling, allowing for flexible task learning and efficient memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If DEQ models maintain the same resolution throughout forward and backward pass, then memory footprint is reduced, but the ability to downsample at intermediate points is lost
Solution Approach 1:
The patent divides the feature processing into multiple resolution streams, segmenting the single-resolution DEQ approach into parallel multi-resolution pathways. This allows different parts of the network to operate at different resolutions, enabling downsampling at intermediate points while maintaining memory efficiency through shared equilibrium solving across the segmented streams.
Solution Approach 2:
The patent introduces a resolution dimension to the traditional DEQ architecture by processing features at multiple resolutions simultaneously. This dimensional expansion allows the model to perform hierarchical feature fusion while maintaining the memory efficiency of DEQ through shared equilibrium solving, resolving the contradiction between memory footprint and downsampling capability.
2Productivity
If DEQ models do not explicitly define multiple layers, then computational efficiency is improved, but hierarchical learning capability is reduced
Solution Approach 1:
The patent makes the equilibrium solver universal by applying it across multiple resolution streams simultaneously. This single equilibrium solving operation serves multiple functions: it processes features at different resolutions, enables hierarchical feature fusion, and maintains computational efficiency. The multi-functional approach resolves the contradiction by achieving hierarchical learning without requiring separate explicit layers for each resolution level.
Solution Approach 2:
The patent merges multiple resolution streams into a unified equilibrium solving process. By combining the processing of different resolution features through shared equilibrium constraints and fusion operations, the model achieves hierarchical learning capabilities while maintaining the computational efficiency of a single DEQ framework, avoiding the need for separate explicit layer structures.
3Adaptability or versatility
If explicit layers are defined for combining features at certain resolutions, then hierarchical feature learning is improved, but memory requirements increase with number of layers
Solution Approach 1:
The patent uses feature fusion operations that combine features from different resolution streams without creating separate explicit layer copies. The equilibrium solving process efficiently merges features through shared computations and fusion operations, achieving hierarchical feature learning while avoiding the memory overhead of duplicating layer structures across multiple resolutions.
Solution Approach 2:
The patent discards intermediate high-resolution features that are no longer needed and recovers essential information through fusion operations at lower resolutions. This selective discarding and recovery process enables hierarchical feature learning while managing memory requirements by eliminating redundant high-resolution data after it has served its purpose in the equilibrium solving process.
Data Source
AI summary
A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.