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07/04/2023
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Few-shot image generation (FSIG) aims to learn to generate new and diver...
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05/31/2023
Efficient Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from ...
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05/26/2023
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unpreced...
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05/03/2023
Nonparametric Generative Modeling with Conditional and Locally-Connected Sliced-Wasserstein Flows
Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric g...
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04/13/2023
CoSDA: Continual Source-Free Domain Adaptation
Without access to the source data, source-free domain adaptation (SFDA) ...
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03/17/2023
A Recipe for Watermarking Diffusion Models
Recently, diffusion models (DMs) have demonstrated their advantageous po...
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02/21/2023
On Calibrating Diffusion Probabilistic Models
Recently, diffusion probabilistic models (DPMs) have achieved promising ...
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02/09/2023
Better Diffusion Models Further Improve Adversarial Training
It has been recognized that the data generated by the denoising diffusio...
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02/09/2023
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving mor...
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01/28/2023
Does Federated Learning Really Need Backpropagation?
Federated learning (FL) is a general principle for decentralized clients...
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05/26/2022
O(N^2) Universal Antisymmetry in Fermionic Neural Networks
Fermionic neural network (FermiNet) is a recently proposed wavefunction ...
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03/13/2022
Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior
Adversarial attacks have been extensively studied in recent years since ...
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03/09/2022
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
Recent studies have revealed the vulnerability of face recognition model...
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02/21/2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
The trade-off between robustness and accuracy has been widely studied in...
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10/15/2021
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial e...
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10/13/2021
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
The vulnerability of deep neural networks to adversarial examples has mo...
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07/05/2021
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can effectively evaluate model robust...
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06/18/2021
Accumulative Poisoning Attacks on Real-time Data
Collecting training data from untrusted sources exposes machine learning...
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06/03/2021
Exploring Memorization in Adversarial Training
It is well known that deep learning models have a propensity for fitting...
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05/31/2021
Adversarial Training with Rectified Rejection
Adversarial training (AT) is one of the most effective strategies for pr...
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03/24/2021
Black-box Detection of Backdoor Attacks with Limited Information and Data
Although deep neural networks (DNNs) have made rapid progress in recent ...
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10/01/2020
Bag of Tricks for Adversarial Training
Adversarial training (AT) is one of the most effective strategies for pr...
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03/15/2020
Towards Privacy Protection by Generating Adversarial Identity Masks
As billions of personal data such as photos are shared through social me...
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02/20/2020
Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses to impro...
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02/14/2020
Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to impr...
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12/26/2019
Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becom...
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09/25/2019
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
It has been widely recognized that adversarial examples can be easily cr...
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06/17/2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior
We consider the black-box adversarial setting, where the adversary has t...
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05/25/2019
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires la...
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04/05/2019
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Deep neural networks are vulnerable to adversarial examples, which can m...
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01/25/2019
Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on variou...
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03/31/2018
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine...
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02/26/2018
Max-Mahalanobis Linear Discriminant Analysis Networks
A deep neural network (DNN) consists of a nonlinear transformation from ...
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12/08/2017
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Neural networks are vulnerable to adversarial examples. This phenomenon ...
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10/17/2017
