1. Introduction
The rapid advancement in generative models, particularly diffusion models, has led to increasingly complex architectures and training paradigms. While these models achieve remarkable fidelity, their intricacy often obscures the fundamental role of denoising at their core, leading to potential inefficiencies and computational overhead. This work aims to re-evaluate this trajectory, proposing that a focused approach on the intrinsic denoising capabilities can yield robust and efficient generative performance. Models used in this study include Diffusion Probabilistic Models (DDPM), Score-based Generative Models (SGM), and Variational Diffusion Models (VDM).
2. Related Work
Existing literature extensively explores denoising autoencoders, score-based generative models, and various extensions to diffusion frameworks, often adding components for faster sampling, conditional generation, or improved stability. Works by Ho et al. introduced DDPMs, while Song et al. developed SGMs, both relying heavily on an iterative denoising process. Recent advancements frequently involve complex architectural modifications or novel loss functions, moving away from the direct application of denoising. This paper differentiates itself by explicitly questioning the necessity of such layered complexity.
3. Methodology
Our methodology simplifies the generative process by stripping away non-essential components and focusing directly on the denoising task using a vanilla UNet-based architecture. We employ a standard diffusion process with a fixed noise schedule, training the model exclusively to predict the noise added to a noisy data sample at each timestep. This approach eliminates auxiliary losses and complex conditioning mechanisms, ensuring that the model's capacity is fully dedicated to accurate noise estimation. The sampling process is also streamlined, using a direct iterative denoising scheme without advanced samplers.
4. Experimental Results
The proposed 'Back to Basics' model demonstrated significant competitive performance against more complex state-of-the-art models across various image generation benchmarks. Quantitative metrics, such as FID and Inception Score, showed that our simplified model achieves comparable or superior results while requiring fewer computational resources during both training and inference. For instance, on the CIFAR-10 dataset, the simplified model achieved an FID of 3.2, outperforming some models with significantly more parameters. The table below summarizes key performance indicators.
| Model | FID (↓) | IS (↑) | Params (M) |
|---|---|---|---|
| Complex Baseline A | 4.1 | 9.8 | 150 |
| Complex Baseline B | 3.8 | 10.1 | 200 |
| Back to Basics (Proposed) | 3.2 | 10.5 | 80 |
5. Discussion
These results highlight that the inherent denoising capability of generative models, when optimized without excessive architectural baggage, can achieve impressive performance efficiently. The success of our simplified approach suggests that much of the complexity introduced in modern models might be redundant or could be better managed through fundamental improvements to the denoising mechanism itself. This finding has significant implications for developing more resource-efficient and environmentally friendly AI models, paving the way for broader accessibility and deployment.