ETL: Efficient Transfer Learning for Face Tasks

Thrupthi Ann John[1], Isha Dua[1], Vineeth N Balasubramanian[2] and C.V. Jawahar[1]

IIIT Hyderabad[1] IIT Hyderabad[2]

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Pipeline for efficient transfer of parameters from model trained on primary task like face-recognition to model for secondary task including gender, emotion, head pose and age in one pass. The ETL technique identifies and preserves the task related filters only which in turn results in highly sparse network for efficient training of face related tasks.



Transfer learning is a popular method for obtaining deep trained models for data-scarce face tasks such as head pose and emotion. However, current transfer learning methods are inefficient and time-consuming as they do not fully account for the relationships between related tasks. Moreover, the transferred model is large and computationally expensive. As an alternative, we propose ETL: a technique that efficiently transfers a pre-trained model to a new task by retaining only \emph{cross-task aware filters}, resulting in a sparse transferred model. We demonstrate the effectiveness of ETL by transferring VGGFace, a popular face recognition model to four diverse face tasks. Our experiments show that we attain a size reduction up to 97\% and an inference time reduction up to 94\% while retaining 99.5\% of the baseline transfer learning accuracy.


Related Publications

ETL: Efficient Transfer Learning for Face tasks

Thrupthi Ann John, Isha Dua, Vineeth N Balasubramanian and C. V. Jawahar
ETL: Efficient Transfer Learning for Face Tasks , 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2022.  [ PDF ] , [ BibTeX ]


For any queries about the work, please contact the authors below

  1. Thrupthi Ann John - thrupthi [dot] ann [at] research [dot] iiit [dot] ac [dot] in
  2. Isha Dua: duaisha1994 [at] gmail [dot] com