Domain-Agnostic Latent Diffusion Models for High-Quality Implicit Neural Representations

Core Concepts

Proposing DDMI, a domain-agnostic latent diffusion model for synthesizing high-quality implicit neural representations across various signal domains.

Abstract

Abstract: Introducing DDMI to address limitations in existing generative models for implicit neural representations (INRs). Proposing adaptive positional embeddings instead of neural networks' weights. Demonstrating superior performance compared to existing INR generative models across four modalities. Introduction: INRs provide flexibility and expressivity in representing arbitrary signals. Recent research focuses on INR generative models using Normalizing Flows, GANs, and Diffusion Models. Existing models exhibit limitations in achieving high-quality results due to fixed positional embeddings. Methodology: Presenting DDMI for synthesizing high-quality INRs with adaptive positional embeddings. Introducing Discrete-to-continuous space Variational AutoEncoder (D2C-VAE) and Hierarchically Decomposed Basis Fields (HDBFs). Describing the training procedure involving VAE training and diffusion model training. Experiments: Evaluating DDMI across 2D images, 3D shapes, and videos. Comparing results with domain-specific and domain-agnostic baselines. Conducting quantitative analysis using metrics like FID, MMD, COV, and qualitative analysis through visualizations. Analysis: Analyzing the decomposition of HDBFs to capture signals of different scales effectively. Conducting an ablation study to evaluate the impact of each component in DDMI. Conclusion: Summarizing the effectiveness of DDMI in synthesizing high-quality INRs across various signal domains.

Stats

"Extensive experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance Fields, and videos" "Code is available at https://github.com/mlvlab/DDMI."

Quotes

Key Insights Distilled From

DDMI

by Dogyun Park,... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2401.12517.pdf

Deeper Inquiries

How can the concept of adaptive positional embeddings be applied in other domains beyond computer science

The concept of adaptive positional embeddings can be applied in various domains beyond computer science, such as natural language processing, biology, finance, and manufacturing. In natural language processing, adaptive positional embeddings can enhance the performance of models like transformers by allowing them to better capture the relationships between words based on their positions in a sentence or document. This could lead to more accurate language understanding and generation tasks. In biology, adaptive positional embeddings could be used in genomics research to improve the analysis of DNA sequences. By incorporating information about the position of genes or genetic variations within a genome, researchers could gain deeper insights into how different regions contribute to biological functions. In finance, adaptive positional embeddings could help in analyzing time-series data more effectively. By considering the temporal order of financial transactions or market movements, models could make better predictions and decisions for trading strategies or risk management. In manufacturing, adaptive positional embeddings could optimize processes by taking into account spatial arrangements of components in production lines or warehouses. This information could improve efficiency and resource allocation in manufacturing operations.

What are potential challenges or drawbacks of relying on fixed positional embeddings in generative models

Relying on fixed positional embeddings in generative models may pose several challenges and drawbacks: Limited Expressiveness: Fixed positional embeddings may restrict the model's ability to capture complex patterns that vary across different positions within a sequence or image. Lack of Adaptability: Models with fixed positional embeddings may struggle when dealing with diverse datasets that require flexible representations based on varying contexts. Generalization Issues: Fixed positional embeddings might not generalize well across different tasks or datasets since they are static representations that do not adapt to specific requirements. Performance Limitations: Generative models relying on fixed positional embeddings may face difficulties generating high-quality outputs for tasks requiring fine-grained details or precise localization.

How might the use of hierarchical decomposition enhance expressive power in other types of neural representations

The use of hierarchical decomposition can enhance expressive power in other types of neural representations by enabling multi-scale feature learning and capturing intricate details at different levels: Image Processing: In image processing tasks like object detection or segmentation, hierarchical decomposition can help extract features at multiple scales (e.g., edges at finer scales and textures at coarser scales) leading to improved accuracy and robustness. Audio Analysis: In audio signal processing applications such as speech recognition or music generation, hierarchical decomposition can aid in capturing both short-term acoustic features (phonemes) as well as long-term contextual information (intonation patterns). Graph Neural Networks: For graph-based data like social networks or molecular structures, hierarchical decomposition can facilitate learning features from local neighborhoods up to global graph structures improving node classification and link prediction tasks. 4..Video Understanding: In video analysis tasks such as action recognition or video synthesis, hierarchical decomposition allows modeling motion dynamics at various temporal resolutions enhancing the model's capability to understand complex spatio-temporal patterns efficiently.