แนวคิดหลัก
Leveraging the capabilities of diffusion models, this paper proposes a technique to generate diverse and photorealistic images based on textual inputs, enabling effective data augmentation to improve the out-of-domain generalization of deep learning models.
บทคัดย่อ
The paper explores the challenge of data scarcity faced by deep learning models, particularly in the context of image classification tasks. To address this, the authors propose a semantic augmentation approach that utilizes diffusion models to generate new images based on modified captions.
The key components of the approach are:
Caption Generation:
Caption label extraction: The authors use BERT to identify the closest word in the caption to the desired class label, enabling targeted modifications.
Augmentation methods: Four strategies are employed - prefix, suffix, replacement, and compound augmentation - to generate new captions by modifying the original ones.
Image Generation:
The authors leverage the Stable Diffusion model to generate photorealistic images corresponding to the augmented captions.
The generated images are stored in the COCO dataset format for convenient integration into the training pipeline.
Augmentation:
The generated images are incorporated into the original COCO Captions dataset during the training of the classification models.
The authors explore the impact of the number of augmented images per original image, aiming to strike a balance between enriching the dataset and avoiding overrepresentation.
The authors conduct experiments to evaluate the in-domain and out-of-domain performance of their approach, comparing it against state-of-the-art techniques like Mixup and AugMix. The results demonstrate the superior performance of the semantic augmentation approach, particularly in enhancing the out-of-domain generalization capabilities of the deep learning models.
สถิติ
The paper does not provide specific numerical data or metrics in the main text. The key results are presented in the form of tables comparing the performance of different models on in-domain (COCO) and out-of-domain (PASCAL VOC) datasets.
คำพูด
The paper does not contain any direct quotes that are particularly striking or support the key logics.