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Rapid progress is being made in developing large, pretrained, task-agnostic foundational vision models such as CLIP, ALIGN, DINOv2, etc.
“Intriguing properties of neural networks”
Christian Szegedy et al · 2014
Earlier work this paper cites.
“Explaining and Harnessing Adversarial Examples”
Ian. Goodfellow, Jonathon Shlens and Christian Szegedy · 2015
Earlier work this paper cites.
“Convergent Learning: Do different neural networks learn the same representations?”
Yixuan Li et al · 2016
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“A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks”
Dan Hendrycks and Kevin Gimpel · 2017
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“Towards Evaluating the Robustness of Neural Networks”
Nicholas Carlini and David. Wagner · 2017
Earlier work this paper cites.
“Delving into Transferable Adversarial Examples and Black-box Attacks”
Yanpei Liu, Xinyun Chen, Chang Liu and Dawn Song · 2017
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“Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks”
Shiyu Liang, Yixuan Li and R. Srikant · 2018
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“Towards Deep Learning Models Resistant to Adversarial Attacks”
Aleksander Madry et al · 2018
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“Boosting Adversarial Attacks With Momentum”
Yinpeng Dong et al · 2018
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“Ensemble Adversarial Training: Attacks and Defenses”
Florian Tramèr et al · 2018
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“Feature Space Perturbations Yield More Transferable Adversarial Examples”
Nathan Inkawhich, Wei Wen, Hai Li and Yiran Chen · 2019
Earlier work this paper cites.
“Analyzing the Robustness of Open-World Machine Learning”
Vikash Sehwag et al · 2019
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“Improving Transferability of Adversarial Examples With Input Diversity”
Cihang Xie et al · 2019
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“Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks”
Yinpeng Dong, Tianyu Pang, Hang Su and Jun Zhu · 2019
Earlier work this paper cites.
“Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability”
Nathan Inkawhich et al · 2020
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“Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks”
David Stutz, Matthias Hein and Bernt Schiele · 2020
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“A Simple Framework for Contrastive Learning of Visual Representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey. Hinton · 2020
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“Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning”
Jean-Bastien Grill et al · 2020
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“Transferable Perturbations of Deep Feature Distributions”
Nathan Inkawhich, Kevin Liang, Lawrence Carin and Yiran Chen · 2020
Earlier work this paper cites.
“Certifiably Adversarially Robust Detection of Out-of-Distribution Data”
Julian Bitterwolf, Alexander Meinke and Matthias Hein · 2020
Cited alongside, same era.
“Learning Transferable Visual Models From Natural Language Supervision”
Alec Radford et al · 2021
Cited alongside, same era.
“Emerging Properties in Self-Supervised Vision Transformers”
Mathilde Caron et al · 2021
Cited alongside, same era.
“Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”
Chao Jia et al · 2021
Cited alongside, same era.
“Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?”
Nathan Inkawhich et al · 2021
Cited alongside, same era.
“Adversarial examples for the OpenAI CLIP in its zero-shot classification regime and their semantic generalization”, 2021
“Robust Out-of-distribution Detection for Neural Networks”
Jiefeng Chen et al · 2022
Later among the works it cites.
“Adversarial Distributions Against Out-of-Distribution Detectors”, 2022
Sangwoong Yoon et al · 2022
Later among the works it cites.
“A ConvNet for the 2020s”
Zhuang Liu et al · 2022
Later among the works it cites.
“DINOv2: Learning Robust Visual Features without Supervision”, 2023
Maxime Oquab et al · 2023
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“Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling”
Keyu Tian et al · 2023
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Stanislav Fort · 2021
Cited alongside, same era.
“Pixels still beat text: Attacking the OpenAI CLIP model with text patches and adversarial pixel perturbations”, 2021
Stanislav Fort · 2021
Cited alongside, same era.
“Reading Isn’t Believing: Adversarial Attacks On Multi-Modal Neurons”, 2021
David. Noever and Samantha. Noever · 2021
Cited alongside, same era.
“ATOM: Robustifying Out-of-Distribution Detection Using Outlier Mining”
Jiefeng Chen et al · 2021
Cited alongside, same era.
“Generating Out of Distribution Adversarial Attack Using Latent Space Poisoning”
Ujjwal Upadhyay and Prerana Mukherjee · 2021
Cited alongside, same era.
“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”
Alexey Dosovitskiy et al · 2021
Cited alongside, same era.
“Revisiting Weakly Supervised Pre-Training of Visual Perception Models”
Mannat Singh et al · 2022
Cited alongside, same era.
Matthias Minderer, Alexey Gritsenko and Neil Houlsby · 2023
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“ImageBind: One Embedding Space To Bind Them All”, 2023
Rohit Girdhar et al · 2023
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“OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection”, 2023
Jingyang Zhang et al · 2023
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“A Cookbook of Self-Supervised Learning”, 2023
Randall Balestriero et al · 2023
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Alexander Kirillov et al · 2023
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“Poisoning Web-Scale Training Datasets is Practical”, 2023
Nicholas Carlini et al · 2023
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“Understanding Zero-Shot Adversarial Robustness for Large-Scale Models”
Chengzhi Mao et al · 2023
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“Language-Driven Anchors for Zero-Shot Adversarial Robustness”, 2023
Xiao Li et al · 2023
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“Towards Out-of-Distribution Adversarial Robustness”, 2023
Adam Ibrahim et al · 2023
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“Scikit-learn Logistic Regression Model” Accessed: April 2023., https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
2023
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“Scikit-learn k-Nearest Neighbors” Accessed: April 2023., https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
2023
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“CLIP Interrogator”, 2023
pharmapsychotic · 2023
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“Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification”
Randolph Linderman et al · 2023
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