Neural Network Latent Code Discovery with Ordered Categorical Representation
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Solution Overview
Problem
Current methods for discovering latent factors in data using neural networks are limited by their reliance on distributed codes, lack of control over code position, and insufficient handling of sensitive data privacy, with existing techniques not effectively utilizing ordered codes or secure data processing.
Innovation Solution
The method involves adding noise to input data using a noise unit, computing latent codes with stochastic non-linear units, and decoding them to reconstruct data, while treating codes as categorical variables and using a translation network and classification model to generate data with desired attributes, and employing homomorphic encryption for secure pre-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed codes are used to represent data, then classification tasks can be performed efficiently, but hierarchical relationships in the data cannot be revealed
Solution Approach 1:
The patent changes the parameter of code representation from distributed continuous values to ordered categorical variables. By imposing an ordering constraint on the latent codes and treating them as categorical variables rather than continuous distributions, the system enables both efficient classification and revelation of hierarchical relationships in the data.
2Adaptability or versatility
If the entire network is trained together with conditional embeddings, then the model can adapt to conditional data generation, but retraining is required whenever conditional embeddings change
Solution Approach 1:
The patent segments the neural network into distinct components: an encoder that generates latent codes, a decoder that reconstructs data, and a separate translation network that handles conditional embeddings. This modular architecture allows the translation network to be updated independently when conditional embeddings change, avoiding the need to retrain the entire network while maintaining adaptability to conditional data generation.
3Productivity
If conventional neural networks are used for sensitive data, then processing can be performed, but data privacy and security are compromised
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer between the sensitive data and the neural network processing. The encoder and subsequent processing operations work on encrypted data without decryption, allowing the network to process sensitive information while maintaining data privacy and security throughout the computation pipeline.
Data Source
AI summary
Aspects of the disclosure relate to a method for discovering latent factors from data in a neural network environment. Aspects include adding, by a data noise unit of the neural network environment, noise to a set of input data; computing, by the encoder model and a set of stochastic non-linear units, a set of latent code based on the set of input data; obtaining, by decoding the latent code with a decoder model, a set of reconstructed data.