Efficient Training with Denoised Neural Weights (2407.11966v1)

Abstract: Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challenging and may require manual tuning, which can be time-consuming and prone to human error. To overcome such limitations, this work takes a novel step towards building a weight generator to synthesize the neural weights for initialization. We use the image-to-image translation task with generative adversarial networks (GANs) as an example due to the ease of collecting model weights spanning a wide range. Specifically, we first collect a dataset with various image editing concepts and their corresponding trained weights, which are later used for the training of the weight generator. To address the different characteristics among layers and the substantial number of weights to be predicted, we divide the weights into equal-sized blocks and assign each block an index. Subsequently, a diffusion model is trained with such a dataset using both text conditions of the concept and the block indexes. By initializing the image translation model with the denoised weights predicted by our diffusion model, the training requires only 43.3 seconds. Compared to training from scratch (i.e., Pix2pix), we achieve a 15x training time acceleration for a new concept while obtaining even better image generation quality.

• The paper presents a novel diffusion-based approach for synthesizing initial weights that significantly accelerates GAN training.
• It reports a 15× speedup over standard training and a 4.6× improvement compared to other efficient GAN methods.
• This method minimizes manual tuning and offers a generalizable framework for enhancing neural network training across varied tasks.

Efficient Training with Denoised Neural Weights: An Insightful Overview

This paper, authored by Yifan Gong et al., proposes a novel approach to reducing the training costs associated with Deep Neural Networks (DNNs) by leveraging denoised neural weights for initialization. Traditionally, good weight initialization is pivotal for stabilizing training, accelerating convergence, and enhancing generalization. However, choosing the right initialization parameters often involves manual tuning, which is both time-consuming and error-prone. The authors address this challenge by introducing a weight generator capable of synthesizing optimal initial weights across varied tasks, particularly focusing on Generative Adversarial Networks (GANs) for image-to-image translation tasks.

Methodology

The authors use GANs for image-to-image translation as a practical example due to the extensive and diverse range of weights available from various trained models. The initial dataset comprises several image editing concepts and their corresponding trained weights. Key to the methodology is the division of these weights into equal-sized blocks, which are then indexed. A diffusion model—utilizing both the concept's text conditions and block indices—is trained on this dataset to predict denoised weights. The model aims to initialize the image translation tasks efficiently.

The proposed framework includes several significant steps:

1. Data Collection: The authors curated an extensive dataset by using LLMs for style generation and augmentation to ensure diversity. This dataset spans various artistic, characteristic, and facial modification concepts.
2. Model Architecture: The weight generator is designed using a UNet architecture equipped with a diffusion process. The model incorporates sinusoidal positional encodings to provide block positional information during the denoising process.
3. Inference and Fine-Tuning: For new concepts, weight initialization involves a single-step denoising process, followed by fine-tuning. This approach drastically reduces the training time compared to conventional methods.

Numerical Results

The numerical results presented in the paper are compelling. By initializing the GAN weights with the denoised predictions from the model, the training time is reduced significantly to 43.3 seconds. This represents a 15× acceleration compared to Pix2pix training from scratch, while simultaneously achieving superior image generation quality. Further, the paper reports a reduction of training time by 4.6× compared to another efficient GAN method, E$^2$GAN, with improved performance metrics.

Implications and Future Perspectives

The implications of this research are multifaceted:

• Practical Efficiency: The substantial reduction in training time translates to lower computational costs, which is critical for large-scale deployment and practical applications in industries where computational resources are a constraint.
• Generalization Across Tasks: While the methodology is exemplified using GANs for image-to-image translation, the framework is potentially generalizable across various deep learning tasks that benefit from efficient weight initialization.
• Reduced Manual Intervention: Automated weight generation mitigates the human error and time expenditure associated with manual parameter tuning, leading to more reliable and reproducible outcomes in model training.

Looking ahead, there are several avenues for future research and potential development:

• Broader Application Domains: Extension of the weight generator's applicability to other domains, such as natural language processing or reinforcement learning, could be investigated.
• Enhanced Data Collection Techniques: Improving the efficiency and quality of the data collection process, possibly by incorporating more advanced diffusion models, could further enhance the predicted weight quality.
• Hybrid Models: Exploring hybrid models that combine diffusion processes with other generative methods may yield even better initial weight predictions.

Conclusion

The methodology presented in this paper highlights a significant step forward in the quest for efficient DNN training through improved weight initialization. The authors' innovative use of a diffusion model to predict neural weights, combined with a well-structured data collection and training process, offers a promising direction for further research and practical implementation. As such, this work not only provides strong numerical results but also opens up new avenues for enhancing the efficiency of neural network training across diverse applications.