title: Transfer Learning for Fine-grained Classification Using Semi-supervised
    Learning and Visual Transformers
summary: 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.
paper_authors._id: 646590a05cdb9ab50f8787fe
paper_authors.name: Manuel Lagunas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-19T14:33:59.472Z
paper_authors._id: 646780b78334813a7ae45eff
paper_authors.avatarUrl: /avatars/b344c7382b3c8cbc5532b986c360ab04.svg
paper_authors.fullname: Manuel Lagunas
paper_authors.type: user
paper_authors.user: mlgns
paper_authors._id: 646590a05cdb9ab50f8787ff
paper_authors.name: Brayan Impata
paper_authors._id: 646590a05cdb9ab50f878800
paper_authors.name: Victor Martinez
paper_authors._id: 646590a05cdb9ab50f878801
paper_authors.name: Virginia Fernandez
paper_authors._id: 646590a05cdb9ab50f878802
paper_authors.name: Christos Georgakis
paper_authors._id: 646590a05cdb9ab50f878803
paper_authors.name: Sofia Braun
paper_authors._id: 646590a05cdb9ab50f878804
paper_authors.name: Felipe Bertrand
paper_title: Transfer Learning for Fine-grained Classification Using Semi-supervised
    Learning and Visual Transformers
paper_summary: 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.
paper_ai_summary: semi-supervised learning, Visual Transformers (ViT), self-attention mechanisms, fine-grained classification, e-commerce, noisy labels, convolutional neural networks (CNN)
paper_ai_keywords: Visual Transformers (ViT) self-attention mechanisms semi-supervised learning convolutional neural networks (CNN)