Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement (2303.08983v3)

Abstract: We propose Dataset Reinforcement, a strategy to improve a dataset once such that the accuracy of any model architecture trained on the reinforced dataset is improved at no additional training cost for users. We propose a Dataset Reinforcement strategy based on data augmentation and knowledge distillation. Our generic strategy is designed based on extensive analysis across CNN- and transformer-based models and performing large-scale study of distillation with state-of-the-art models with various data augmentations. We create a reinforced version of the ImageNet training dataset, called ImageNet+, as well as reinforced datasets CIFAR-100+, Flowers-102+, and Food-101+. Models trained with ImageNet+ are more accurate, robust, and calibrated, and transfer well to downstream tasks (e.g., segmentation and detection). As an example, the accuracy of ResNet-50 improves by 1.7% on the ImageNet validation set, 3.5% on ImageNetV2, and 10.0% on ImageNet-R. Expected Calibration Error (ECE) on the ImageNet validation set is also reduced by 9.9%. Using this backbone with Mask-RCNN for object detection on MS-COCO, the mean average precision improves by 0.8%. We reach similar gains for MobileNets, ViTs, and Swin-Transformers. For MobileNetV3 and Swin-Tiny, we observe significant improvements on ImageNet-R/A/C of up to 20% improved robustness. Models pretrained on ImageNet+ and fine-tuned on CIFAR-100+, Flowers-102+, and Food-101+, reach up to 3.4% improved accuracy. The code, datasets, and pretrained models are available at https://github.com/apple/ml-dr.

• The paper introduces Dataset Reinforcement, a strategy that leverages data augmentation and knowledge distillation to enhance model accuracy and robustness with no additional training overhead.
• It demonstrates that models trained on reinforced datasets like ImageNet⁺ can achieve accuracy gains of up to 1.7% and robustness improvements of up to 20% across various architectures.
• The approach also improves transfer learning performance by up to 3.4%, maintaining standard training pipelines while boosting overall model efficiency.

Overview of Dataset Reinforcement for Improved Model Training

The paper "Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement" introduces Dataset Reinforcement (DR), an innovative strategy designed to enhance dataset utility and model performance across various architectures without incurring additional training costs for end-users. This approach leverages data augmentation and knowledge distillation techniques to create a single-enhanced dataset that can be repeatedly used to train different model architectures with improved accuracy and robustness.

Dataset Reinforcement is demonstrated through the creation of augmented datasets such as ImageNet$^+$, CIFAR-100$^+$, Flowers-102$^+$, and Food-101$^+$. These datasets encapsulate the benefits of training on large datasets with strong models, while maintaining accessibility for smaller models and datasets that traditionally cannot scale due to data collection and annotation costs.

Key Findings

1. Improvement Across Architectures: Models trained on reinforced datasets exhibit significant improvements in accuracy and robustness. For instance, models trained on ImageNet$^+$ demonstrate substantial gains—ResNet-50 shows an increase of 1.7% on ImageNet, 3.5% on ImageNetV2, and 10% on ImageNet-R. Similarly, MobileNetV3 and Swin-Tiny achieve up to 20% improved robustness on various ImageNet variations.
2. Knowledge Distillation and Augmentation: The paper conducts a comprehensive analysis of multiple state-of-the-art models to identify effective teachers for knowledge distillation. It demonstrates that ensembles of state-of-the-art models perform better as teachers across different architectures than single models. This finding corroborates the hypothesis that large models and diverse ensembles can generalize better than smaller models or single approach methodologies.
3. Minimal User Overhead: One of the critical advantages of DR is that it adds no training overhead compared to traditional training setups. Reinforced datasets do not require any changes in model training code except the path alteration to load the dataset, making it conveniently reusable. This approach ensures minimal training cost and computational efficiency for users.
4. Enhanced Transfer Learning: When pretrained on ImageNet$^+$ and fine-tuned on reinforced versions of other datasets like CIFAR-100$^+$, models show improved performance of up to 3.4% compared to traditional pretraining techniques. This indicates the potential of reinforced datasets to enhance the effectiveness of transfer learning across various domains.

Implications and Future Developments

The DR strategy marks a significant advance in leveraging data redundancy and model ensembling to create generically improved datasets. It ameliorates the challenge of model-specific data augmentation and distillation overheads, thus democratizing access to performance gains typically locked behind computationally expensive practices.

Furthermore, the strategy potentially eases the transition to the deployment phase of machine learning models by maintaining consistency in inference time while increasing model reliability and calibration across unseen data distributions.

Looking forward, the approach opens avenues for integrating emerging generative AI models for creating synthetic reinforcements, further extending the domain of applicability beyond classical vision tasks to multimodal and cross-disciplinary applications. Moreover, future work can explore more refined control over reinforcement difficulty and tailor it further based on the specific architecture and application needs, thus personalizing reinforcement.

This paper’s contributions establish a framework that is seminal not only for its immediate practical benefits but also for its potential to chart future explorations in AI model training efficiencies and capabilities.