Semantic Augmentation in Images using Language
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911151635300352 |
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| author | Yerramilli, Sahiti Tamarapalli, Jayant Sravan Kulkarni, Tanmay Girish Francis, Jonathan Nyberg, Eric |
| author_facet | Yerramilli, Sahiti Tamarapalli, Jayant Sravan Kulkarni, Tanmay Girish Francis, Jonathan Nyberg, Eric |
| contents | Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Leveraging the substantial datasets used to train these diffusion models, we propose a technique to utilize generated images to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Augmentation in Images using Language Yerramilli, Sahiti Tamarapalli, Jayant Sravan Kulkarni, Tanmay Girish Francis, Jonathan Nyberg, Eric Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limiting their ability to generalize to real-world examples. Recent advancements in diffusion models have enabled the generation of photorealistic images based on textual inputs. Leveraging the substantial datasets used to train these diffusion models, we propose a technique to utilize generated images to augment existing datasets. This paper explores various strategies for effective data augmentation to improve the out-of-domain generalization capabilities of deep learning models. |
| title | Semantic Augmentation in Images using Language |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.02353 |