Transfer Learning for Fine-grained Classification Using Semi-supervised
  Learning and Visual Transformers
Fine-grained classification is a challenging task that involves identifying
subtle differences between objects within the same category. This task is
particularly challenging in scenarios where data is scarce. Visual transformers
(ViT) have recently emerged as a powerful tool for image classification, due to
their ability to learn highly expressive representations of visual data using
self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine
tuned using semi-supervised learning techniques, suitable for situations where
we have lack of annotated data. This is particularly common in e-commerce,
where images are readily available but labels are noisy, nonexistent, or
expensive to obtain. Our results demonstrate that Semi-ViT outperforms
traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned
with limited annotated data. These findings indicate that Semi-ViTs hold
significant promise for applications that require precise and fine-grained
classification of visual data.
646590a05cdb9ab50f8787fe
Manuel Lagunas
claimed_verified
2023-05-19T14:33:59.472Z
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/avatars/b344c7382b3c8cbc5532b986c360ab04.svg
Manuel Lagunas
user
mlgns
646590a05cdb9ab50f8787ff
Brayan Impata
646590a05cdb9ab50f878800
Victor Martinez
646590a05cdb9ab50f878801
Virginia Fernandez
646590a05cdb9ab50f878802
Christos Georgakis
646590a05cdb9ab50f878803
Sofia Braun
646590a05cdb9ab50f878804
Felipe Bertrand
Transfer Learning for Fine-grained Classification Using Semi-supervised
  Learning and Visual Transformers
Fine-grained classification is a challenging task that involves identifying
subtle differences between objects within the same category. This task is
particularly challenging in scenarios where data is scarce. Visual transformers
(ViT) have recently emerged as a powerful tool for image classification, due to
their ability to learn highly expressive representations of visual data using
self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine
tuned using semi-supervised learning techniques, suitable for situations where
we have lack of annotated data. This is particularly common in e-commerce,
where images are readily available but labels are noisy, nonexistent, or
expensive to obtain. Our results demonstrate that Semi-ViT outperforms
traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned
with limited annotated data. These findings indicate that Semi-ViTs hold
significant promise for applications that require precise and fine-grained
classification of visual data.
semi-supervised learning, Visual Transformers (ViT), self-attention mechanisms, fine-grained classification, e-commerce, noisy labels, convolutional neural networks (CNN)
Visual Transformers (ViT) self-attention mechanisms semi-supervised learning convolutional neural networks (CNN)