Conditional Normalizing Flow Priors for Multi-Modal CVAE Learning
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Solution Overview
Problem
Existing conditional variational autoencoders (CVAEs) face issues such as over-regularization, difficulty in capturing multi-modal distributions, and latent variable collapse, leading to unimodal generations and poor learning of probability distributions, particularly in complex environments like real-world traffic scenes.
Innovation Solution
A machine learnable system using a variational autoencoder with flexible priors and normalizing flows, where the encoder and decoder functions generate parameters defining a probability distribution with a predetermined variance, and a sequence of invertible normalizing flow sub-functions, improves the learning of complex probability distributions, especially in latent spaces with multiple modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a standard Gaussian prior is used in CVAE, then the model structure is simple, but the model suffers from over-regularization and difficulty in capturing multi-modal distributions
Solution Approach 1:
The patent changes the prior distribution parameter from a standard Gaussian to a flexible normalizing flow-based distribution. This allows the model to adapt to complex multi-modal distributions while maintaining a structured approach through the flow-based transformation framework.
Solution Approach 2:
The patent introduces a normalizing flow as an intermediary transformation between the latent space and the prior distribution. This mediator enables the model to handle complex distributions without directly modifying the core CVAE architecture, thus maintaining simplicity while improving adaptability.
2Adaptability or versatility
If CVAE is used for generation, then the model can handle conditional data, but latent variable collapse occurs and minor modes are not captured
Solution Approach 1:
The patent modifies the latent variable distribution parameters by using a flexible prior with normalizing flows, which prevents collapse to a single mode and enables reliable representation of multiple conditional modes including minor modes.
Solution Approach 2:
The patent introduces dynamic flexibility to the prior distribution through normalizing flows, allowing the latent variable distribution to adapt its shape and complexity based on the conditional input, thereby preventing collapse while maintaining conditional generation capability.
3Adaptability or versatility
If the encoder uses learnable variance, then the model has more flexibility, but training becomes unstable and learning is hindered
Solution Approach 1:
The patent changes the variance parameter from a learnable quantity to a fixed predetermined value. This simplifies the training landscape and improves stability while the flexibility is maintained through the normalizing flow transformation in the prior distribution rather than through variance learning.
Data Source
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AI summary
Some embodiments are directed to a machine learnable system (110). A conditional normalizing flow function maps a latent representation (z) to a base point (e=f(z,c)) in a base space (E) conditional on conditioning data (c). The conditional normalizing flow function is a machine learnable function and trained on a set of training pairs.