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Deep Neural Networks for Image-Based Dietary Assessment
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Learning optimal image representations through noise injection for fine-grained search.

Vidit Kumar1, Vikas Tripathi1, Bhaskar Pant1

  • 1Department of CSE, Graphic Era Deemed to be University, Dehradun, India.

Scientific Reports
|May 3, 2025
PubMed
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This study introduces a novel noise injection method to improve fine-grained image search. By adding noise to images and features, the approach enhances representation learning and achieves superior retrieval results.

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Fine-grained image search is a growing area of interest.
  • Current methods often use deep feature learning with embeddings, but face challenges like expensive sampling (triplet loss) or early saturation (softmax loss).

Purpose of the Study:

  • To enhance fine-grained representation learning for image search.
  • To address limitations of existing loss functions like triplet loss and softmax loss.

Main Methods:

  • A novel approach incorporating noise injection into both input images and deep features.
  • Reducing the distance between L2 normalized features of original and noisy images in the embedding space.
  • Using feature noise as regularization to prevent overfitting and promote generalized features.
Keywords:
Feature learningFine-grained image retrievalImage representationNoise injectionZero-shot learning

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Main Results:

  • Achieved superior retrieval results on Oxford flower-17, Cub-200-2011, and Cars-196 datasets compared to existing methods.
  • Demonstrated favorable performance in the Zero-Shot setting on Cars-196 and Cub-200-2011.

Conclusions:

  • The proposed noise injection technique effectively enhances fine-grained representation learning.
  • This method offers a promising alternative for improving image search accuracy and generalization.