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Marcella Astrid
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2020 – today
- 2024
- [j3]Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee:
Exploiting autoencoder's weakness to generate pseudo anomalies. Neural Comput. Appl. 36(23): 14075-14091 (2024) - [i17]Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem, Enjie Ghorbel
, Djamila Aouada:
LAA-Net: Localized Artifact Attention Network for High-Quality Deepfakes Detection. CoRR abs/2401.13856 (2024) - [i16]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data. CoRR abs/2403.16270 (2024) - [i15]Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee:
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies. CoRR abs/2405.05886 (2024) - [i14]Marcella Astrid, Enjie Ghorbel, Djamila Aouada:
Targeted Augmented Data for Audio Deepfake Detection. CoRR abs/2407.07598 (2024) - [i13]Marcella Astrid, Enjie Ghorbel, Djamila Aouada:
Statistics-aware Audio-visual Deepfake Detector. CoRR abs/2407.11650 (2024) - 2023
- [j2]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies. Neurocomputing 534: 147-160 (2023) - [i12]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies. CoRR abs/2303.10704 (2023) - 2022
- [j1]Muhammad Zaigham Zaheer
, Jin Ha Lee, Arif Mahmood
, Marcella Astrid
, Seung-Ik Lee
:
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies. IEEE Trans. Image Process. 31: 5963-5975 (2022) - [c14]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies. UR 2022: 248-251 - [i11]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos. CoRR abs/2203.13704 (2022) - [i10]Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies. CoRR abs/2203.13716 (2022) - 2021
- [c13]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Learning Not to Reconstruct Anomalies. BMVC 2021: 279 - [c12]Jin Ha Lee, Marcella Astrid
, Muhammad Zaigham Zaheer
, Seung-Ik Lee
:
Deep Visual Anomaly Detection with Negative Learning. IW-FCV 2021: 218-232 - [c11]Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee:
Small Object Detection using Context and Attention. ICAIIC 2021: 181-186 - [c10]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection. ICCVW 2021: 207-214 - [c9]Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Marcella Astrid, Seung-Ik Lee:
An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration. ICCVW 2021: 2595-2601 - [c8]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Domain-Robust Pedestrian-View Intersection Classification. ICTC 2021: 1087-1090 - [i9]Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Arif Mahmood, Seung-Ik Lee:
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection. CoRR abs/2104.14770 (2021) - [i8]Jin Ha Lee, Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Deep Visual Anomaly detection with Negative Learning. CoRR abs/2105.11058 (2021) - [i7]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Learning Not to Reconstruct Anomalies. CoRR abs/2110.09742 (2021) - [i6]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection. CoRR abs/2110.09768 (2021) - 2020
- [c7]Jin-Ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee:
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers. CVPR Workshops 2020: 3264-3274 - [c6]Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Seung-Ik Lee:
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm. CVPR 2020: 14171-14181 - [c5]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection. ECCV (22) 2020: 358-376 - [c4]Marcella Astrid, Jin Ha Lee, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
For Safer Navigation: Pedestrian-View Intersection Classification. ICTC 2020: 7-10 - [i5]Muhammad Zaigham Zaheer, Jin Ha Lee, Marcella Astrid, Seung-Ik Lee:
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm. CoRR abs/2004.07657 (2020) - [i4]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection. CoRR abs/2011.12077 (2020)
2010 – 2019
- 2019
- [i3]Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee:
Small Object Detection using Context and Attention. CoRR abs/1912.06319 (2019) - 2018
- [c3]Marcella Astrid, Seung-Ik Lee, Beom-Su Seo:
Rank selection of CP-decomposed convolutional layers with variational Bayesian matrix factorization. ICACT 2018: 347-350 - [c2]Hwa-Yeon Kim, Jinsu Lee, Na Young Yeo, Marcella Astrid, Seung-Ik Lee, Young-Kil Kim:
CNN based Sentence Classification with Semantic Features using Word Clustering. ICTC 2018: 484-488 - [i2]Marcella Astrid, Seung-Ik Lee, Beom-Su Seo:
Rank Selection of CP-decomposed Convolutional Layers with Variational Bayesian Matrix Factorization. CoRR abs/1801.05243 (2018) - 2017
- [c1]Marcella Astrid, Seung-Ik Lee:
CP-decomposition with Tensor Power Method for Convolutional Neural Networks compression. BigComp 2017: 115-118 - [i1]Marcella Astrid, Seung-Ik Lee:
CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression. CoRR abs/1701.07148 (2017)
Coauthor Index
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last updated on 2024-08-27 22:56 CEST by the dblp team
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