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J. Piland

9 papers hereh-index 563 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • cs.LG2
  • q-bio.MN2

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedImproving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing 2023Show all

3 papers · 1 filter

cs.LG2023

Non-Generative Energy Based Models

Jacob Piland, Christopher Sweet, Priscila Saboia +2

Energy-based models (EBM) have become increasingly popular within computer vision. EBMs bring a probabilistic approach to training deep neural networks (DNN) and have been shown to…

cs.CV2023

Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models

Colton Crum, Patrick Tinsley, Aidan Boyd +5

The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult…

cs.CV2023★ 1 cited

Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors

Jacob Piland, Adam Czajka, Christopher Sweet

Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an obser…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.