Online Convolutional Re-parameterization (2204.00826v1)

Abstract: Structural re-parameterization has drawn increasing attention in various computer vision tasks. It aims at improving the performance of deep models without introducing any inference-time cost. Though efficient during inference, such models rely heavily on the complicated training-time blocks to achieve high accuracy, leading to large extra training cost. In this paper, we present online convolutional re-parameterization (OREPA), a two-stage pipeline, aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution. To achieve this goal, we introduce a linear scaling layer for better optimizing the online blocks. Assisted with the reduced training cost, we also explore some more effective re-param components. Compared with the state-of-the-art re-param models, OREPA is able to save the training-time memory cost by about 70% and accelerate the training speed by around 2x. Meanwhile, equipped with OREPA, the models outperform previous methods on ImageNet by up to +0.6%.We also conduct experiments on object detection and semantic segmentation and show consistent improvements on the downstream tasks. Codes are available at https://github.com/JUGGHM/OREPA_CVPR2022 .

• The paper presents a novel two-stage online re-parameterization method that replaces norm layers with linear scaling layers to cut training memory by 70% and double speed.
• The approach improves CNN accuracy by up to +0.6% on ImageNet and consistently enhances downstream tasks like object detection and semantic segmentation.
• The study opens avenues for exploring more complex re-parameterized architectures without prohibitive costs, fostering scalable and efficient deep learning models.

Overview of Online Convolutional Re-parameterization

The paper entitled "Online Convolutional Re-parameterization" by Mu Hu et al. addresses the challenges associated with structural re-parameterization in convolutional neural networks (CNNs) with a focus on reducing training costs while maintaining computational efficiency during inference. Structural re-parameterization strategies are designed to achieve performance gains without introducing inference-time overhead. However, such methods often necessitate complex, resource-intensive training regimes.

Key Contributions

The authors propose a novel two-stage pipeline termed Online Convolutional Re-parameterization (OREPA), which significantly reduces the additional training cost typically incurred by re-parameterization models. The process involves replacing the norm layers used during training with linear scaling layers, which are simpler and maintain the diversity crucial for optimizing differing branches of re-parameterization blocks.

Highlights of OREPA

1. Training Efficiency: The OREPA approach reduces training-time memory burden by about 70% and enhances training speed by up to 2x, compared to state-of-the-art re-parameterization models.
2. Improved Performance: When equipped with OREPA, models show improved performance, with increases in accuracy by up to +0.6% on ImageNet classification tasks.
3. Effective for Downstream Tasks: The effectiveness of OREPA extends beyond classification to include tasks like object detection and semantic segmentation, maintaining consistent gains.
4. Opportunities for More Complex Architectures: The reduction in training cost with OREPA mechanisms makes it possible to explore more complex re-parameterized architectures without prohibitive cost increases.

Analysis and Implications

This work makes a significant contribution to the field by exploring how structural transformations can be leveraged for improved model training practices. The focus on minimizing training costs while maintaining a high level of inference efficiency aligns well with ongoing trends towards more efficient model design. Notably, the OREPA strategy stands to facilitate experimentation with more elaborate re-parameterized topologies, which could yield further gains in model capability and performance.

The introduction of linear scaling layers as replacements for traditional normalization within branches is a key innovation, showing that such replacements do not compromise model performance and, in fact, support enhanced optimization diversity among branches. This finding challenges the conventional reliance on non-linear normalization layers during training, suggesting alternative pathways to achieve similar benefits.

Implications for Future Research

For future research, exploring the applicability of these linear scaling techniques in other forms of neural network architectures beyond CNNs could be insightful. Additionally, adapting the OREPA approach in contexts requiring even larger models or extensive datasets will test its scalability and efficacy across various machine learning environments.

The paper opens avenues for continued investigation into structural re-parameterization, emphasizing efficiency at the training stage, a priority given the computational constraints imposed by ever-larger datasets and models. Future work might also involve integrating OREPA with other model compression techniques to further enhance deployment efficiencies across constrained environments.

In summary, this paper provides a robust framework for achieving training efficiency in re-parameterization models, potentially setting the stage for more explorative and resource-effective neural network architectures in the AI community.