5 citations · 15 across the 7 of their papers we have counts for
9 papers
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu, Wan-Yi Lin, Devin Willmott +4
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data…
Defending Multimodal Fusion Models against Single-Source Adversaries
Karren Yang, Wan-Yi Lin, Manash Barman +2
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information bet…
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Ameya Joshi, Minh Pham, Minsu Cho +4
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require aug…
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami +2
Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the…
Provably robust deep generative models
Filipe Condessa, Zico Kolter
Recent work in adversarial attacks has developed provably robust methods for training deep neural network classifiers. However, although they are often mentioned in the context of…
Image Classification with Rejection using Contextual Information
Filipe Condessa, José Bioucas-Dias, Carlos Castro +2
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information. Rejection is desired in image-classification applications t…
Robust hyperspectral image classification with rejection fields
Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyperspectral image classification is generally an ill-posed imag…
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
We present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure te…
Performance measures for classification systems with rejection
Filipe Condessa, Jelena Kovacevic, Jose Bioucas-Dias
Classifiers with rejection are essential in real-world applications where misclassifications and their effects are critical. However, if no problem specific cost function is define…