Data Fine-Tuning


  • Saheb Chhabra Indian Institute of Technology Delhi
  • Puspita Majumdar Indian Institute of Technology Delhi
  • Mayank Vatsa Indian Institute of Technology Delhi
  • Richa Singh Indian Institute of Technology Delhi



In real-world applications, commercial off-the-shelf systems are utilized for performing automated facial analysis including face recognition, emotion recognition, and attribute prediction. However, a majority of these commercial systems act as black boxes due to the inaccessibility of the model parameters which makes it challenging to fine-tune the models for specific applications. Stimulated by the advances in adversarial perturbations, this research proposes the concept of Data Fine-tuning to improve the classification accuracy of a given model without changing the parameters of the model. This is accomplished by modeling it as data (image) perturbation problem. A small amount of “noise” is added to the input with the objective of minimizing the classification loss without affecting the (visual) appearance. Experiments performed on three publicly available datasets LFW, CelebA, and MUCT, demonstrate the effectiveness of the proposed concept.




How to Cite

Chhabra, S., Majumdar, P., Vatsa, M., & Singh, R. (2019). Data Fine-Tuning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8223-8230.



AAAI Technical Track: Vision