F-Mixup: Attack CNNs from Fourier Perspective
Xiu-Chuan Li,
Xu-Yao Zhang,
Fei Yin,
Cheng-Lin Liu
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in
session PS T1.13

Auto-TLDR; F-Mixup: A novel black-box attack in frequency domain for deep neural networks
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Defense Mechanism against Adversarial Attacks Using Density-Based Representation of Images
Yen-Ting Huang,
Wen-Hung Liao,
Chen-Wei Huang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.3

Auto-TLDR; Adversarial Attacks Reduction Using Input Recharacterization
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Adversarially Training for Audio Classifiers
Raymel Alfonso Sallo,
Mohammad Esmaeilpour,
Patrick Cardinal
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in
session PS T1.8

Auto-TLDR; Adversarially Training for Robust Neural Networks against Adversarial Attacks
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Attack Agnostic Adversarial Defense via Visual Imperceptible Bound
Saheb Chhabra,
Akshay Agarwal,
Richa Singh,
Mayank Vatsa
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.4

Auto-TLDR; Robust Adversarial Defense with Visual Imperceptible Bound
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Variational Inference with Latent Space Quantization for Adversarial Resilience
Vinay Kyatham,
Deepak Mishra,
Prathosh A.P.
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in
session PS T1.9

Auto-TLDR; A Generalized Defense Mechanism for Adversarial Attacks on Data Manifolds
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Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods
Jaka Šircelj,
Danijel Skocaj
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 16:00 in
session PS T1.15

Auto-TLDR; Accuracy-perturbation Curve for Robustness Evaluation of Adversarial Examples
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Verifying the Causes of Adversarial Examples
Honglin Li,
Yifei Fan,
Frieder Ganz,
Tony Yezzi,
Payam Barnaghi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in
session PS T1.12

Auto-TLDR; Exploring the Causes of Adversarial Examples in Neural Networks
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Optimal Transport As a Defense against Adversarial Attacks
Quentin Bouniot,
Romaric Audigier,
Angélique Loesch
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in
session PS T1.7

Auto-TLDR; Sinkhorn Adversarial Training with Optimal Transport Theory
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Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers
Yi Xiang Marcus Tan,
Yuval Elovici,
Alexander Binder
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.1

Auto-TLDR; Adaptive Stochastic Networks for Adversarial Attacks
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Task-based Focal Loss for Adversarially Robust Meta-Learning
Yufan Hou,
Lixin Zou,
Weidong Liu
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in
session PS T1.14

Auto-TLDR; Task-based Adversarial Focal Loss for Few-shot Meta-Learner
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A Delayed Elastic-Net Approach for Performing Adversarial Attacks
Brais Cancela,
Veronica Bolon-Canedo,
Amparo Alonso-Betanzos
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 14:00 in
session PS T1.5

Auto-TLDR; Robustness of ImageNet Pretrained Models against Adversarial Attacks
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Cost-Effective Adversarial Attacks against Scene Text Recognition
Mingkun Yang,
Haitian Zheng,
Xiang Bai,
Jiebo Luo
Track 4: Document and Media Analysis
Thu 14 Jan 2021 at 12:00 in
session PS T4.3

Auto-TLDR; Adversarial Attacks on Scene Text Recognition
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Beyond Cross-Entropy: Learning Highly Separable Feature Distributions for Robust and Accurate Classification
Arslan Ali,
Andrea Migliorati,
Tiziano Bianchi,
Enrico Magli
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in
session PS T1.9

Auto-TLDR; Gaussian class-conditional simplex loss for adversarial robust multiclass classifiers
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AdvHat: Real-World Adversarial Attack on ArcFace Face ID System
Stepan Komkov,
Aleksandr Petiushko
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in
session PS T2.2

Auto-TLDR; Adversarial Sticker Attack on ArcFace in Shooting Conditions
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Transferable Adversarial Attacks for Deep Scene Text Detection
Shudeng Wu,
Tao Dai,
Guanghao Meng,
Bin Chen,
Jian Lu,
Shutao Xia
Track 4: Document and Media Analysis
Fri 15 Jan 2021 at 13:00 in
session OS T 4.2

Auto-TLDR; Robustness of DNN-based STD methods against Adversarial Attacks
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Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Mahmoud Hossam,
Le Trung,
He Zhao,
Dinh Phung
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.4

Auto-TLDR; Transfer2Attack: A Black-box Adversarial Attack on Text Classification
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Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks
Ramin Barati,
Reza Safabakhsh,
Mohammad Rahmati
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 16:00 in
session PS T1.15

Auto-TLDR; Convolutional Neural Networks and Adversarial Training from the Perspective of convergence
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CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection
Shengnan Hu,
Yang Zhang,
Sumit Laha,
Ankit Sharma,
Hassan Foroosh
Track 3: Computer Vision Robotics and Intelligent Systems
Tue 12 Jan 2021 at 17:00 in
session PS T3.3

Auto-TLDR; Contextual camouflage attack for object detection
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Polynomial Universal Adversarial Perturbations for Person Re-Identification
Wenjie Ding,
Xing Wei,
Rongrong Ji,
Xiaopeng Hong,
Yihong Gong
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in
session PS T1.13

Auto-TLDR; Polynomial Universal Adversarial Perturbation for Re-identification Methods
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On-Manifold Adversarial Data Augmentation Improves Uncertainty Calibration
Kanil Patel,
William Beluch,
Dan Zhang,
Michael Pfeiffer,
Bin Yang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.2

Auto-TLDR; On-Manifold Adversarial Data Augmentation for Uncertainty Estimation
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Killing Four Birds with One Gaussian Process: The Relation between Different Test-Time Attacks
Kathrin Grosse,
Michael Thomas Smith,
Michael Backes
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 14:00 in
session PS T1.6

Auto-TLDR; Security of Gaussian Process Classifiers against Attack Algorithms
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On the Robustness of 3D Human Pose Estimation
Zerui Chen,
Yan Huang,
Liang Wang
Track 2: Biometrics, Human Analysis and Behavior Understanding
Fri 15 Jan 2021 at 13:00 in
session OS T2.3

Auto-TLDR; Robustness of 3D Human Pose Estimation Methods to Adversarial Attacks
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Attack-Agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
Matthew Watson,
Noura Al Moubayed
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in
session PS T5.2

Auto-TLDR; Explainability-based Detection of Adversarial Samples on EHR and Chest X-Ray Data
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Learning with Multiplicative Perturbations
Xiulong Yang,
Shihao Ji
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in
session PS T1.1

Auto-TLDR; XAT and xVAT: A Multiplicative Adversarial Training Algorithm for Robust DNN Training
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MINT: Deep Network Compression Via Mutual Information-Based Neuron Trimming
Madan Ravi Ganesh,
Jason Corso,
Salimeh Yasaei Sekeh
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in
session PS T1.7

Auto-TLDR; Mutual Information-based Neuron Trimming for Deep Compression via Pruning
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How Does DCNN Make Decisions?
Yi Lin,
Namin Wang,
Xiaoqing Ma,
Ziwei Li,
Gang Bai
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.3

Auto-TLDR; Exploring Deep Convolutional Neural Network's Decision-Making Interpretability
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Removing Backdoor-Based Watermarks in Neural Networks with Limited Data
Xuankai Liu,
Fengting Li,
Bihan Wen,
Qi Li
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 15:00 in
session PS T1.13

Auto-TLDR; WILD: A backdoor-based watermark removal framework using limited data
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Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
Gary Shing Wee Goh,
Sebastian Lapuschkin,
Leander Weber,
Wojciech Samek,
Alexander Binder
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.1

Auto-TLDR; SmoothGrad: bridging Integrated Gradients and SmoothGrad from the Taylor's theorem perspective
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Local Clustering with Mean Teacher for Semi-Supervised Learning
Zexi Chen,
Benjamin Dutton,
Bharathkumar Ramachandra,
Tianfu Wu,
Ranga Raju Vatsavai
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in
session OS T1.3

Auto-TLDR; Local Clustering for Semi-supervised Learning
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Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection
Faisal Alamri,
Sinan Kalkan,
Nicolas Pugeault
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in
session PS T3.6

Auto-TLDR; Context Module for Robust Object Detection with Transformer-Encoder Detector Module
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Delving in the Loss Landscape to Embed Robust Watermarks into Neural Networks
Enzo Tartaglione,
Marco Grangetto,
Davide Cavagnino,
Marco Botta
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in
session PS T1.1

Auto-TLDR; Watermark Aware Training of Neural Networks
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Boundary Optimised Samples Training for Detecting Out-Of-Distribution Images
Luca Marson,
Vladimir Li,
Atsuto Maki
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 16:00 in
session PS T1.16

Auto-TLDR; Boundary Optimised Samples for Out-of-Distribution Input Detection in Deep Convolutional Networks
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Improving Gravitational Wave Detection with 2D Convolutional Neural Networks
Siyu Fan,
Yisen Wang,
Yuan Luo,
Alexander Michael Schmitt,
Shenghua Yu
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in
session PS T5.2

Auto-TLDR; Two-dimensional Convolutional Neural Networks for Gravitational Wave Detection from Time Series with Background Noise
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Knowledge Distillation Beyond Model Compression
Fahad Sarfraz,
Elahe Arani,
Bahram Zonooz
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 14:00 in
session PS T1.11

Auto-TLDR; Knowledge Distillation from Teacher to Student
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Watermelon: A Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy
Xiang Xie,
Wilhelm Stork
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in
session PS T1.1

Auto-TLDR; Feature Selection Using Bayes Error Rate Estimation for Dynamic Feature Selection
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MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
Yangbin Chen,
Yun Ma,
Tom Ko,
Jianping Wang,
Qing Li
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 14:00 in
session PS T1.5

Auto-TLDR; MetaMix: A Meta-Agnostic Meta-Learning Algorithm for Few-Shot Classification
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Discriminative Multi-Level Reconstruction under Compact Latent Space for One-Class Novelty Detection
Jaewoo Park,
Yoon Gyo Jung,
Andrew Teoh
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.2

Auto-TLDR; Discriminative Compact AE for One-Class novelty detection and Adversarial Example Detection
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Adversarially Constrained Interpolation for Unsupervised Domain Adaptation
Mohamed Azzam,
Aurele Tohokantche Gnanha,
Hau-San Wong,
Si Wu
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in
session PS T1.10

Auto-TLDR; Unsupervised Domain Adaptation with Domain Mixup Strategy
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Adversarial Training for Aspect-Based Sentiment Analysis with BERT
Akbar Karimi,
Andrea Prati,
Leonardo Rossi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in
session PS T1.7

Auto-TLDR; Adversarial Training of BERT for Aspect-Based Sentiment Analysis
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Face Anti-Spoofing Using Spatial Pyramid Pooling
Lei Shi,
Zhuo Zhou,
Zhenhua Guo
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in
session PS T2.2

Auto-TLDR; Spatial Pyramid Pooling for Face Anti-Spoofing
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Generalization Comparison of Deep Neural Networks Via Output Sensitivity
Mahsa Forouzesh,
Farnood Salehi,
Patrick Thiran
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.1

Auto-TLDR; Generalization of Deep Neural Networks using Sensitivity
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Revisiting Graph Neural Networks: Graph Filtering Perspective
Hoang Nguyen-Thai,
Takanori Maehara,
Tsuyoshi Murata
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.4

Auto-TLDR; Two-Layers Graph Convolutional Network with Graph Filters Neural Network
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Generative Latent Implicit Conditional Optimization When Learning from Small Sample
Idan Azuri,
Daphna Weinshall
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in
session PS T1.7

Auto-TLDR; GLICO: Generative Latent Implicit Conditional Optimization for Small Sample Learning
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Color, Edge, and Pixel-Wise Explanation of Predictions Based onInterpretable Neural Network Model
Jay Hoon Jung,
Youngmin Kwon
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 12:00 in
session PS T1.3

Auto-TLDR; Explainable Deep Neural Network with Edge Detecting Filters
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Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification
Federico Pollastri,
Juan Maroñas,
Federico Bolelli,
Giulia Ligabue,
Roberto Paredes,
Riccardo Magistroni,
Costantino Grana
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in
session PS T3.5

Auto-TLDR; A Probabilistic Convolutional Neural Network for Immunofluorescence Classification in Renal Biopsy
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A Joint Representation Learning and Feature Modeling Approach for One-Class Recognition
Pramuditha Perera,
Vishal Patel
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 16:00 in
session PS T1.12

Auto-TLDR; Combining Generative Features and One-Class Classification for Effective One-class Recognition
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A Weak Coupling of Semi-Supervised Learning with Generative Adversarial Networks for Malware Classification
Shuwei Wang,
Qiuyun Wang,
Zhengwei Jiang,
Xuren Wang,
Rongqi Jing
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in
session PS T5.7

Auto-TLDR; IMIR: An Improved Malware Image Rescaling Algorithm Using Semi-supervised Generative Adversarial Network
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ESResNet: Environmental Sound Classification Based on Visual Domain Models
Andrey Guzhov,
Federico Raue,
Jörn Hees,
Andreas Dengel
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Thu 14 Jan 2021 at 16:00 in
session PS T5.6

Auto-TLDR; Environmental Sound Classification with Short-Time Fourier Transform Spectrograms
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Towards Robust Learning with Different Label Noise Distributions
Diego Ortego,
Eric Arazo,
Paul Albert,
Noel E O'Connor,
Kevin Mcguinness
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in
session OS T1.2

Auto-TLDR; Distribution Robust Pseudo-Labeling with Semi-supervised Learning
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Auto Encoding Explanatory Examples with Stochastic Paths
Cesar Ali Ojeda Marin,
Ramses J. Sanchez,
Kostadin Cvejoski,
Bogdan Georgiev
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 14:00 in
session PS T1.11

Auto-TLDR; Semantic Stochastic Path: Explaining a Classifier's Decision Making Process using latent codes
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