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Communication Dans Un Congrès Année : 2020

This dataset does not exist: training models from generated images

Himalaya Jain
  • Fonction : Auteur
  • PersonId : 988661
Andrei Bursuc
Patrick Pérez
  • Fonction : Auteur
  • PersonId : 1022281

Résumé

Current generative networks are increasingly proficient in generating high-resolution realistic images. These generative networks, especially the conditional ones, can potentially become a great tool for providing new image datasets. This naturally brings the question: Can we train a classifier only on the generated data? This potential availability of nearly unlimited amounts of training data challenges standard practices for training machine learning models, which have been crafted across the years for limited and fixed size datasets. In this work we investigate this question and its related challenges. We identify ways to improve significantly the performance over naive training on randomly generated images with regular heuristics. We propose three standalone techniques that can be applied at different stages of the pipeline, i.e., data generation, training on generated data, and deploying on real data. We evaluate our proposed approaches on a subset of the ImageNet dataset and show encouraging results compared to classifiers trained on real images.

Dates et versions

hal-02507436 , version 1 (13-03-2020)

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Citer

Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, Patrick Pérez. This dataset does not exist: training models from generated images. ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain. ⟨hal-02507436⟩
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