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07/08/2023
Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers
Acute Lymphoblastic Leukemia (ALL) is one of the most common types of ch...
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04/14/2023
KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images
Instance-level graph neural network explainers have proven beneficial fo...
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01/25/2023
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Current machine learning models achieve super-human performance in many ...
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10/25/2022
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection
The influenza virus hemagglutinin is an important part of the virus atta...
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09/30/2022
Your Out-of-Distribution Detection Method is Not Robust!
Out-of-distribution (OOD) detection has recently gained substantial atte...
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07/14/2022
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations
In this paper, we propose the physics informed adversarial training (PIA...
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06/09/2022
SwinCheX: Multi-label classification on chest X-ray images with transformers
According to the considerable growth in the avail of chest X-ray images ...
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06/09/2022
OOD Augmentation May Be at Odds with Open-Set Recognition
Despite advances in image classification methods, detecting the samples ...
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05/28/2022
Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models
We aim for image-based novelty detection. Despite considerable progress,...
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10/26/2021
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Machine learning models often encounter samples that are diverged from t...
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07/01/2021
The Interplay between Distribution Parameters and the Accuracy-Robustness Tradeoff in Classification
Adversarial training tends to result in models that are less accurate on...
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03/29/2021
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training
Making deep neural networks robust to small adversarial noises has recen...
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03/29/2021
Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training
Recent improvements in deep learning models and their practical applicat...
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11/22/2020
Multiresolution Knowledge Distillation for Anomaly Detection
Unsupervised representation learning has proved to be a critical compone...
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08/29/2020
Puzzle-AE: Novelty Detection in Images through Solving Puzzles
Autoencoder (AE) has proved to be an effective framework for novelty det...
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03/30/2020
Towards Deep Learning Models Resistant to Large Perturbations
Adversarial robustness has proven to be a required property of machine l...
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03/12/2020
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
Autoencoders (AE) have recently been widely employed to approach the nov...
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01/29/2013