Instance-Adaptive Parametrization for Amortized Variational Inference
Summary: arXiv:2604.06796v1 Announce Type: cross
Abstract
Latent variable models, including variational autoencoders (VAE), remain a central tool in modern deep generative modeling due to their scalability and a well-founded probabilistic formulation. These models rely on amortized variational inference to enable efficient posterior approximation, but this efficiency comes at the cost of a shared parametrization, giving rise to the amortization gap.
Introduction
In the realm of deep generative modeling, variational autoencoders (VAEs) have become increasingly popular due to their capacity for scalability and robust probabilistic foundations. However, while amortized variational inference allows for rapid posterior approximations, it introduces a significant limitation known as the amortization gap. This gap arises from the shared parametrization across inputs, which can lead to suboptimal performance in certain scenarios.
Proposed Solution
To address this issue, we introduce the instance-adaptive variational autoencoder (IA-VAE), a novel framework that enhances amortized variational inference through the use of a hypernetwork. This hypernetwork generates input-dependent modulations of a shared encoder, thereby allowing for the adaptation of the inference model to each specific input while maintaining the efficiency of a single forward pass.
Key Features of IA-VAE
- Instance-specific Parameter Modulations: By leveraging modulations that are tailored to individual inputs, IA-VAE can better capture the nuances of the data, leading to improved posterior approximations.
- Efficiency: The architecture preserves the benefits of amortized inference by facilitating a single forward pass, thereby reducing computational overhead.
- Performance: Initial experiments demonstrate that IA-VAE can match the performance of standard VAEs while utilizing significantly fewer parameters, indicating a more efficient allocation of model capacity.
Experimental Results
To validate the effectiveness of the IA-VAE, we conducted experiments on both synthetic datasets, where the true posterior is known, and standard image benchmarks. The results from these experiments highlight several key findings:
- IA-VAE consistently yields more accurate posterior approximations compared to traditional VAE models.
- The method significantly reduces the amortization gap, thereby enhancing overall model performance.
- On standard image datasets, IA-VAE demonstrates statistically significant improvements in held-out evidence lower bound (ELBO) over baseline VAEs across multiple trials.
Conclusion
The introduction of instance-adaptive modulation within the IA-VAE framework represents a significant advancement in mitigating the challenges posed by amortization-induced suboptimality in deep generative models. By enhancing the flexibility of the inference parametrization, IA-VAE offers a promising solution for improving the efficiency and accuracy of variational inference methods in practical applications.