90 citations · 149 across the 19 of their papers we have counts for
11 papers · 1 filter
Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility
William Paul, Philip Mathew, Fady Alajaji +1
This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision. Regarding privacy, we look at this importan…
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Haolin Yuan, Bo Hui, Yuchen Yang +3
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs…
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation
Haolin Yuan, Armin Hadzic, William Paul +5
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assist…
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…
AI Fairness via Domain Adaptation
Neil Joshi, Phil Burlina
While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployme…
Addressing Visual Search in Open and Closed Set Settings
Nathan Drenkow, Philippe Burlina, Neil Fendley +2
Searching for small objects in large images is a task that is both challenging for current deep learning systems and important in numerous real-world applications, such as remote s…
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis
Nathan Drenkow, Neil Fendley, Philippe Burlina
Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be wel…
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
Neil Fendley, Max Lennon, I-Jeng Wang +2
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via…
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection
William Paul, I-Jeng Wang, Fady Alajaji +1
Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controll…
Deep Learning based Retinal OCT Segmentation
Mike Pekala, Neil Joshi, David E. Freund +3
Our objective is to evaluate the efficacy of methods that use deep learning (DL) for the automatic fine-grained segmentation of optical coherence tomography (OCT) images of the ret…
Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning
Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina +1
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation. For both non-nov…