DNN Activation Vector Encoding for Specific Predictions
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Solution Overview
Problem
Existing deep neural networks (DNNs) face challenges in making sensitive and specific predictions due to limitations in memory encodings, which assume rigid sampling structures and mix information from unique space-time locations, making it difficult to adapt to new data contexts and predict labels associated with specific objects or scenes.
Innovation Solution
The proposed system employs a novel data encoding strategy that optimally encodes and retrieves DNN activation layers, allowing for sensitive and specific predictions. This involves storing DNN activation vectors at space-time points along with prediction labels and non-image information, enabling the indexing of similar activation vectors for prediction based on highly specific content shared by new images and previous examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If DNN activations are encoded with rigid sampling structures (global descriptors, pooling operations), then computational efficiency is improved, but the ability to retrieve and predict for specific space-time locations deteriorates
Solution Approach 1:
The patent segments the DNN activation layer into multiple spatio-temporal bins, allowing separate encoding and retrieval for each location. This segmentation enables the system to maintain computational efficiency through structured encoding while improving prediction specificity by retrieving activations from matching spatio-temporal locations rather than mixing all activations together.
Solution Approach 2:
The patent applies local quality by treating different spatio-temporal locations with different encoding strategies. Each bin receives tailored encoding based on its specific characteristics, allowing the system to optimize for both efficiency (through consistent binning) and specificity (through location-aware retrieval) simultaneously.
2Reliability
If DNN filters are optimized to minimize average error across training set, then overall accuracy is improved, but information regarding specific input image samples is sacrificed
Solution Approach 1:
The patent performs preliminary action by storing the full DNN activation vectors for all training images in a database before prediction time. This preserves all specific sample information that would otherwise be lost during filter optimization, allowing the system to retrieve exact matches during prediction while maintaining the accuracy benefits of optimized filters.
Solution Approach 2:
The patent creates copies of the DNN activation vectors and stores them in an external database. This copying mechanism preserves the complete information from each training sample without requiring the original data to be retained in memory, enabling both accurate predictions and information preservation.
3Adaptability or versatility
If multiple DNNs are trained for each condition or value of non-image data, then prediction adaptability is improved, but the curse of dimensionality and need for large training sets worsens
Solution Approach 1:
The patent creates a universal prediction system that can handle multiple conditions and values of non-image data through a single DNN trained on augmented data. By including non-image data as additional input channels and using spatio-temporal binning to organize predictions, the system achieves multi-functionality without requiring separate networks for each condition, thereby reducing training complexity while maintaining adaptability.
4Quantity of substance
If DNN activations are encoded into compressed descriptors, then memory usage is reduced, but the ability to index and retrieve specific activations deteriorates
Solution Approach 1:
The patent segments activations into spatio-temporal bins and stores compact representations for each bin rather than full high-dimensional vectors. This segmentation allows efficient memory usage through compact bin-level storage while maintaining retrieval capability by preserving the structured organization that enables bin-based querying and matching.
Solution Approach 2:
The patent changes the parameter representation by transforming full activation vectors into compressed bin-level descriptors that capture essential information in a more compact form. This parameter transformation reduces memory requirements while preserving the ability to perform meaningful retrieval operations through the structured bin organization.
Data Source
AI summary
Described herein are computer-implemented systems, methods, and apparatuses for predicting classifications for image data, the system comprising a processor, memory and storage, and configured to: receive at least one image data sample; generate, using a deep neural network, for each image data sample, an activation layer comprising a set of activation vectors; perform activation vector correspondence for at least one of the activation vectors based on activation vector distance and conditioned by non-image data; and predict at least one classification for each image data sample based on the activation vector correspondence.