Cue Removal Training Data for Segment Transition Detection
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Solution Overview
Problem
Deep learning models for computer vision struggle to identify segment transitions in data streams without cues, as they are typically trained on data with cues, leading to poor performance when cues are absent, due to the limited availability of annotated data sets without cues.
Innovation Solution
A system comprising a training data set with visual data having synthetic semantic implants, an annotator to identify and annotate cues, and a data scrambler to remove these cues, creating a tagged training data set that can be used to train classifiers to identify transitions without relying on explicit cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are trained on data with cues, then they can accurately identify segment transitions when cues are present, but they fail to identify transitions when cues are absent
Solution Approach 1:
The patent applies preliminary action by pre-processing training data to remove cues before training the model. This allows the model to learn segment transitions based on intrinsic data patterns rather than relying on explicit cues during inference, thereby improving adaptability to cue-absent situations while maintaining training efficiency
Solution Approach 2:
The patent changes the parameter of training data composition by systematically removing or masking cue elements from training samples. This parameter modification forces the model to learn alternative features for segment transition detection, resolving the contradiction between cue-dependent accuracy and cue-independent adaptability
2Adaptability or versatility
If annotated training data without cues is created manually, then the model can learn to identify transitions without cues, but the process is costly and time-consuming
Solution Approach 1:
The system performs preliminary automated processing of training data by detecting and removing cues through algorithmic analysis rather than manual annotation. This preliminary action dramatically increases productivity while still producing the required cue-free training data for improving model adaptability
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system that uses algorithms to identify and remove cues from training data. This substitution of mechanical human labor with automated processing resolves the contradiction between achieving adaptability and maintaining productivity
3Ease of manufacture
If the model learns to rely on cues for segment transition identification, then training is simpler and faster, but the model cannot generalize to situations where cues are not present
Solution Approach 1:
The patent modifies the training data parameter by removing cues, which initially increases training complexity but ultimately simplifies the model's learning task by eliminating spurious dependencies. This parameter change enables the model to learn robust, generalizable features that work across both cue-present and cue-absent situations
Solution Approach 2:
Instead of training the model with cues and hoping it generalizes, the patent inverts the approach by training with cues removed from the beginning. This inverted training strategy forces the model to learn intrinsic patterns directly, achieving both generalization capability and practical training feasibility
Data Source
AI summary
A system and method for generating hard training data from easy training data. Training data including visual data with synthetic semantic implants (“VSSI”) having at least one cue is received. An annotator identifies at least one cue in the VSSI and annotates the VSSI to indicate the cue to create a modified training data set. A data scrambler removes at least one cue from the VSSI to create the tagged training data, which can then be used to train a classifier to identify transitions between segments when the cues are not present.