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Found 17 papers, 10 papers with code
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible.
Ranked #2 on
Domain Generalization
on DomainNet
Boosting Adversarial Robustness using Feature Level Stochastic Smoothing
Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks.
Certified Adversarial Robustness Within Multiple Perturbation Bounds
We further propose a novel \textit{training noise distribution} along with a \textit{regularized training scheme} to improve the certification within both $\ell_1$ and $\ell_2$ perturbation norms simultaneously.
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks
Generalization of neural networks is crucial for deploying them safely in the real world.
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases
The first component considers the frequency of answers within a question type in the training data, which addresses the concern of the class-imbalance causing the language biases.
Efficient and Effective Augmentation Strategy for Adversarial Training
We first explain this contrasting behavior by viewing augmentation during training as a problem of domain generalization, and further propose Diverse Augmentation-based Joint Adversarial Training (DAJAT) to use data augmentations effectively in adversarial training.
Scaling Adversarial Training to Large Perturbation Bounds
The presence of images that flip Oracle predictions and those that do not makes this a challenging setting for adversarial robustness.
Towards Efficient and Effective Self-Supervised Learning of Visual Representations
Self-supervision has emerged as a propitious method for visual representation learning after the recent paradigm shift from handcrafted pretext tasks to instance-similarity based approaches.
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones.
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution.
Towards Data-Free Model Stealing in a Hard Label Setting
In this work, we show that it is indeed possible to steal Machine Learning models by accessing only top-1 predictions (Hard Label setting) as well, without access to model gradients (Black-Box setting) or even the training dataset (Data-Free setting) within a low query budget.
Towards Efficient and Effective Adversarial Training
The vulnerability of Deep Neural Networks to adversarial attacks has spurred immense interest towards improving their robustness.
Towards Achieving Adversarial Robustness Beyond Perceptual Limits
The presence of images that flip Oracle predictions and those that do not, makes this a challenging setting for adversarial robustness.
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Further, we propose Guided Adversarial Training (GAT), which achieves state-of-the-art performance amongst single-step defenses by utilizing the proposed relaxation term for both attack generation and training.
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
Existing visualization methods develop high confidence images consisting of both background and foreground features.
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
In this work, we attempt to address this problem by training networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction.
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier
We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples from a trained classifier, using a novel Data-enriching GAN (DeGAN) framework.
