Multi-task Neural Network Training with Masked Loss
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Solution Overview
Problem
Existing machine learning models face challenges in leveraging existing datasets to train models for multiple classifications due to the extensive time and cost needed for additional ground truth labeling and potential lack of standardization between input image formats.
Innovation Solution
The implementation of a multi-task joint training framework using a multi-headed attention mechanism and an encoder module, which enables the use of diverse training datasets with multi-label annotations and handles heterogeneous labels with missing annotations through a mask-based mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing machine learning models are trained for multiple classifications using existing datasets, then the model can perform multi-task inference, but extensive time and cost are needed for additional ground truth labeling
Solution Approach 1:
The patent applies multi-functionality by designing a neural network with a shared encoder that serves multiple classification tasks simultaneously. The encoder processes input data and generates features that are then distributed to multiple task heads, each performing different classification tasks. This universal encoder architecture eliminates the need for separate models for each task and reduces the requirement for task-specific ground truth labeling, as the shared encoder learns representations that are useful across all tasks.
Solution Approach 2:
The patent segments the neural network into distinct components: a shared encoder module and multiple independent task heads. Each task head is responsible for a specific classification task and receives features from the shared encoder. This segmentation allows each task to be optimized independently while sharing computational resources, reducing the overall time and cost required for training and labeling.
2Adaptability or versatility
If existing machine learning models are trained for multiple classifications, then the model can handle diverse datasets, but there is potential lack of standardization between input image formats
Solution Approach 1:
The shared encoder serves as a universal interface that accepts input data in various formats and transforms them into a standardized feature representation. The encoder's universal nature allows it to handle different input image formats, resolutions, and modalities without requiring separate processing pipelines for each format, thereby simplifying the standardization challenge while maintaining diversity in dataset handling.
Solution Approach 2:
The encoder acts as an intermediary between diverse input formats and the task heads. It receives varied input data, performs format-agnostic feature extraction, and outputs standardized features that can be processed by any task head. This intermediary role simplifies the complexity of handling multiple input formats by centralizing the transformation logic in the encoder.
3Productivity
If neural networks are trained using multi-label datasets with heterogeneous labels, then the model can perform synergistic multi-task learning, but the training process becomes more complex
Solution Approach 1:
The patent merges multiple classification tasks into a single unified training process through the shared encoder architecture. Instead of training separate models for each task or using complex multi-stage pipelines, the encoder and task heads are trained jointly in a single forward pass, with losses from all tasks contributing to the encoder's gradient updates. This merging approach improves joint learning efficiency while keeping the training process relatively simple through automatic differentiation and standard optimization algorithms.
Solution Approach 2:
The patent applies partial action by computing losses for individual tasks independently and then combining them through masking mechanisms. The mask-based loss computation allows the system to handle heterogeneous labels and missing data without requiring perfect alignment across all tasks, simplifying the training process while maintaining the benefits of multi-task learning.
4Reliability
If masks are applied to filter training loss metrics based on labels, then the model can handle missing annotations, but the computation requires additional masking operations
Solution Approach 1:
The patent changes the parameter representation by using binary masks to indicate the presence or absence of labels. Instead of complex handling mechanisms for missing annotations, the system simply uses mask values (0 or 1) to filter losses during computation. This parameter simplification reduces the complexity of handling missing data while maintaining reliability through the mask-based filtering mechanism.
Data Source
AI summary
Systems and methods for multi-task joint training of a neural network including an encoder module and a multi-headed attention mechanism are provided. In one aspect, the system includes a processor configured to receive input data including a first set of labels and a second set of labels. Using the encoder module, features are extracted from the input data. Using a multi-headed attention mechanism, training loss metrics are computed. A first training loss metric is computed using the extracted features and the first set of labels, and a second training loss metric is computed using the extracted features and the second set of labels. A first mask is applied to filter the first training loss metric, and a second mask is applied to filter the second training loss metric. A final training loss metric is computed based on the filtered first and second training loss metrics.