Support-Patch Attention in Few-Shot Autoregressive Neural Networks
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Solution Overview
Problem
Existing neural networks require thousands of examples and training steps to generate data items, limiting their efficiency and applicability in tasks requiring rapid generation and processing.
Innovation Solution
A neural network system employing a causal convolutional neural network with a soft attention mechanism and support memory, allowing generation of data items based on a few examples by iteratively conditioning on previously generated values and attending to relevant context from stored support data patches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional neural networks are used to generate data items, then they can process and generate data, but they require thousands of examples and training steps, which reduces efficiency and increases processing time
Solution Approach 1:
The system performs preliminary encoding of support data into compact representations (support embeddings) before the actual data generation task. This pre-processing allows the network to work with compressed, essential features rather than raw data, significantly reducing the number of training examples and steps needed during the actual generation phase.
Solution Approach 2:
The patent introduces support embeddings as an intermediary representation between the support data and the generation process. These embeddings act as a mediator that captures essential patterns from few examples, enabling the network to generate new data items without requiring thousands of training samples. The attention mechanism further mediates by selectively focusing on relevant parts of the support embeddings.
2Reliability
If thousands of training examples are used, then the neural network can learn patterns, but the processing and memory requirements increase significantly
Solution Approach 1:
The system extracts only the essential patterns and features from the support data by encoding them into compact support embeddings. Instead of storing and processing thousands of full training examples, the network extracts and retains only the critical information needed for generation, dramatically reducing memory requirements while maintaining pattern recognition accuracy.
Solution Approach 2:
The patent transforms the representation parameters of the support data from raw high-dimensional data to compressed embedding vectors. This parameter transformation maintains the essential pattern information while reducing the dimensional space required to store and process the data, enabling efficient operation with minimal memory resources.
3Device complexity
If the neural network uses a fixed context for all generated values, then the architecture is simpler, but it cannot adapt to specific patterns in different parts of the generated data
Solution Approach 1:
The patent implements a dynamic attention mechanism that allows the context to adapt automatically based on the current generation state. Instead of a fixed context, the attention weights dynamically adjust to focus on relevant portions of the support embeddings for each specific generated value, enabling the network to adapt to different patterns without increasing architectural complexity significantly.
Solution Approach 2:
The attention mechanism enables different parts of the generated data to access different relevant contexts from the support embeddings. Each generated value can selectively attend to specific regions or features of the support data that are most relevant to that particular position, providing local adaptation while maintaining overall architectural efficiency.
Data Source
AI summary
A system comprising a causal convolutional neural network to autoregressively generate a succession of values of a data item conditioned upon previously generated values of the data item. The system includes support memory for a set of support data patches each of which comprises an encoding of an example data item. A soft attention mechanism attends to one or more patches when generating the current item value. The soft attention mechanism determines a set of scores for the support data patches, for example in the form of a soft attention query vector dependent upon the previously generated values of the data item. The soft attention query vector is used to query the memory. When generating the value of the data item at a current iteration layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores.