7 citations · 19 across the 8 of their papers we have counts for
8 papers
Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak +4
While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance of each model is limited to its own dat…
on the effectiveness of generative adversarial network on anomaly detection
Laya Rafiee Sevyeri, Thomas Fevens
Identifying anomalies refers to detecting samples that do not resemble the training data distribution. Many generative models have been used to find anomalies, and among them, gene…
Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation
Prabhakara Subramanya Jois, Aniketh Manjunath, Thomas Fevens
Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis of pelvic bone diseases and for planning patient-specific hip surgeries. With the emergence and advanc…
FoCL: Feature-Oriented Continual Learning for Generative Models
Qicheng Lao, Mehrzad Mortazavi, Marzieh Tahaei +3
In this paper, we propose a general framework in continual learning for generative models: Feature-oriented Continual Learning (FoCL). Unlike previous works that aim to solve the c…
The TCGA Meta-Dataset Clinical Benchmark
Mandana Samiei, Tobias Würfl, Tristan Deleu +6
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and…
Dual Adversarial Inference for Text-to-Image Synthesis
Qicheng Lao, Mohammad Havaei, Ahmad Pesaranghader +3
Synthesizing images from a given text description involves engaging two types of information: the content, which includes information explicitly described in the text (e.g., color,…