9 citations · 12 across the 7 of their papers we have counts for
7 papers
APLA: A Simple Adaptation Method for Vision Transformers
Moein Sorkhei, Emir Konuk, Kevin Smith +1
Existing adaptation techniques typically require architectural modifications or added parameters, leading to high computational costs and complexity. We introduce Attention Project…
Efficient Self-Supervised Adaptation for Medical Image Analysis
Moein Sorkhei, Emir Konuk, Jingyu Guo +3
Self-supervised adaptation (SSA) improves foundation model transfer to medical domains but is computationally prohibitive. Although parameter efficient fine-tuning methods such as…
k-NN as a Simple and Effective Estimator of Transferability
Moein Sorkhei, Christos Matsoukas, Johan Fredin Haslum +2
How well can one expect transfer learning to work in a new setting where the domain is shifted, the task is different, and the architecture changes? Many transfer learning metrics…
Learning from Offline Foundation Features with Tensor Augmentations
Emir Konuk, Christos Matsoukas, Moein Sorkhei +2
We introduce Learning from Offline Foundation Features with Tensor Augmentations (LOFF-TA), an efficient training scheme designed to harness the capabilities of foundation models i…
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
Christos Matsoukas, Johan Fredin Haslum, Moein Sorkhei +2
Transfer learning is a standard technique to transfer knowledge from one domain to another. For applications in medical imaging, transfer from ImageNet has become the de-facto appr…
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Moein Sorkhei, Yue Liu, Hossein Azizpour +6
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of…
Full-Glow: Fully conditional Glow for more realistic image generation
Moein Sorkhei, Gustav Eje Henter, Hedvig Kjellström
Autonomous agents, such as driverless cars, require large amounts of labeled visual data for their training. A viable approach for acquiring such data is training a generative mode…