Introducing Dataset Reinforcement: Improving Model Accuracy Effortlessly
Dataset Reinforcement is a groundbreaking method to enhance the accuracy of machine learning models without any additional training costs for users. By combining data augmentation and knowledge distillation, our strategy, based on extensive analysis, unlocks the true potential of models built on convolutional neural networks (CNN) and transformer architecture.
What is Dataset Reinforcement?
Dataset Reinforcement involves strengthening an existing dataset by augmenting it with additional information. By doing so, we can significantly improve the accuracy, robustness, and calibration of the models trained on the reinforced dataset. This enhanced dataset, known as ImageNet+, acts as a catalyst for improved performance in various tasks such as segmentation and object detection.
The Impact of Dataset Reinforcement
Let’s take a closer look at the results of Dataset Reinforcement. When utilizing the ResNet-50 backbone on the ImageNet+ dataset, we observe a remarkable improvement of 1.7% accuracy on the ImageNet validation set, 3.5% on ImageNetV2, and an impressive 10.0% on ImageNet-R. Additionally, the Expected Calibration Error (ECE) of the ResNet-50 model on the ImageNet validation set decreases by 9.9%, indicating superior calibration.
When incorporated into models like Mask-RCNN for object detection on MS-COCO, the mean average precision increases by 0.8%. Similar improvements have been witnessed with other models like MobileNets, ViTs, and Swin-Transformers. In particular, MobileNetV3 and Swin-Tiny models exhibit up to 20% greater robustness on ImageNet-R/A/C.
Furthermore, models pretrained on the ImageNet+ dataset and fine-tuned on datasets such as CIFAR-100+, Flowers-102+, and Food-101+ showcase an impressive boost in accuracy, reaching up to 3.4% improvement.
Dataset Reinforcement revolutionizes the field of machine learning by offering a cost-effective approach to enhance model performance. Its potential applications extend to various domains, opening new doors for AI-driven advancements.