2026-09-19T03:29:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1569532024-09-25T03:12:32Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Madry, Aleksander
Ma, Lingyi
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-24T18:22:40Z
2024-09-24T18:22:40Z
2024-05
2024-07-11T14:37:39.504Z
https://hdl.handle.net/1721.1/156953
The Imagenet dataset is an important benchmark and test bed for computer vision models. Two of its most important characteristics are the size and difficulty, which were what motivated the breakthrough deep learning model Alexnet a decade ago. As researches progress and computation power grows, the best models nowadays can achieve accuracy as high as 90% on Imagenet. With such high accuracy, model predictions are usually of high precision and the causes of this long tail of error are unknown. Many studies have suggested that reassessing Imagenet, a nontrivial amount of label error and noise is found and effort had been made to fix this label noise in the test set, mainly through manual review. However, not many studies have dived into fixing labels for the training set, largely due to its large scale. The proposed thesis aims to understand the remaining errors that models are still making on the ImageNet dataset and investigate the labeling problems in the Imagenet training set, utilizing TRAK- a recently developed efficient data attribution method to help identify problematic images among the 1.4 million images in Imagenet training set.
M.Eng.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Investigation on ImageNet Remaining Errors with TRAK
Thesis
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Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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<Abstract>The Imagenet dataset is an important benchmark and test bed for computer vision models. Two of its most important characteristics are the size and difficulty, which were what motivated the breakthrough deep learning model Alexnet a decade ago. As researches progress and computation power grows, the best models nowadays can achieve accuracy as high as 90% on Imagenet. With such high accuracy, model predictions are usually of high precision and the causes of this long tail of error are unknown. Many studies have suggested that reassessing Imagenet, a nontrivial amount of label error and noise is found and effort had been made to fix this label noise in the test set, mainly through manual review. However, not many studies have dived into fixing labels for the training set, largely due to its large scale. The proposed thesis aims to understand the remaining errors that models are still making on the ImageNet dataset and investigate the labeling problems in the Imagenet training set, utilizing TRAK- a recently developed efficient data attribution method to help identify problematic images among the 1.4 million images in Imagenet training set.</Abstract>
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