Initializing Models with Larger Ones (2311.18823v1)

Abstract: Weight initialization plays an important role in neural network training. Widely used initialization methods are proposed and evaluated for networks that are trained from scratch. However, the growing number of pretrained models now offers new opportunities for tackling this classical problem of weight initialization. In this work, we introduce weight selection, a method for initializing smaller models by selecting a subset of weights from a pretrained larger model. This enables the transfer of knowledge from pretrained weights to smaller models. Our experiments demonstrate that weight selection can significantly enhance the performance of small models and reduce their training time. Notably, it can also be used together with knowledge distillation. Weight selection offers a new approach to leverage the power of pretrained models in resource-constrained settings, and we hope it can be a useful tool for training small models in the large-model era. Code is available at https://github.com/OscarXZQ/weight-selection.

• The paper presents a novel weight selection methodology that transfers pretrained knowledge from large models to effectively initialize smaller models.
• The paper demonstrates that integrating weight selection with knowledge distillation significantly boosts performance across diverse image classification datasets.
• The paper shows that supervised pretraining and closely sized teacher models yield superior initialization compared to conventional methods.

Overview of "Initializing Models with Larger Ones"

The paper "Initializing Models with Larger Ones" investigates the innovative concept of utilizing pretrained large models to initialize smaller models, addressing the traditional bottleneck of weight initialization in neural network training. This work introduces a method called weight selection, which selects weights from a larger, pretrained model to initialize a smaller one within the same model family. The paper emphasizes the significance of such an approach in reducing large model training time and improving the performance of smaller models in resource-constrained environments.

Key Contributions

1. Weight Selection Methodology: The authors propose an elegant weight selection approach, which involves layer selection, component mapping, and element selection. The method selects a subset of weights from a larger pretrained model, initializing a smaller model with these weights. This procedure effectively transfers knowledge from the larger model to the smaller counterpart.
2. Compatibility with Knowledge Distillation: The paper explores the integration of weight selection with knowledge distillation techniques, such as logit-based and feature-based distillation. Results show that combining these techniques amplifies the performance benefits, underscoring the complementary nature of weight selection and knowledge distillation.
3. Comprehensive Evaluation: The authors conduct extensive experiments across nine image classification datasets, spanning various scales, to validate the efficacy of the proposed method. The approach significantly improves test accuracy across all datasets, with larger gains observed in smaller datasets.
4. Analysis of Pretraining Regimes and Teacher Size: By examining different pretrained models and varying the teacher model size, the work provides insights into the nuanced dynamics affecting the initialization quality. Supervised pretraining emerges as the most effective, while closer-sized pretrained models provide better initialization.
5. Empirical Comparisons: The paper offers a robust comparison between weight selection and other initialization strategies, such as Xavier and Kaiming initialization, structured and unstructured pruning, and mimetic initialization. Weight selection consistently demonstrates superior performance, which is attributed to its direct utilization of pretrained weights.

Implications and Future Directions

This research presents significant implications for the efficient deployment of neural networks, particularly in settings with constrained computational resources. The proposed method offers a practical and computationally inexpensive solution to leverage the expansive knowledge encapsulated in large pretrained models, promoting broader applicability of deep learning models on devices with limited resources.

Theoretically, the paper provides a substantial contribution to the understanding of how pretrained models can be optimally utilized beyond standard transfer learning paradigms. It opens new avenues for future research in optimizing weight initialization methods tailored to various model architectures, investigating the potential of hybrid models that integrate multiple knowledge transfer techniques.

Looking forward, the methodology could inspire further exploration into modular designs and adaptive initialization strategies, potentially influencing the development of more efficient algorithms for training lightweight neural networks. The implications extend to the architecture design of models, where the focus could shift towards developing architectures that naturally facilitate weight selection across different scales.

In conclusion, "Initializing Models with Larger Ones" presents a compelling argument for rethinking weight initialization in modern neural networks, leveraging pretrained models to drive substantial improvements in model training efficiency and performance. This work is poised to inspire ongoing exploration into the scalable, efficient training of neural networks, especially in the context of increasingly sophisticated AI applications.