Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting | Paper Citation Record

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This paper cites Language Models are Few-Shot Learners.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Language Models are Few-Shot Learners

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This paper cites Learning transferable visual models from natural language supervision.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Learning transferable visual models from natural language supervision

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting A simple framework for contrastive learning of visual representations

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Emerging properties in self-supervised vision transformers

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Very Deep Convolutional Networks for Large-Scale Image Recognition

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Deep residual learning for image recognition

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Unresolved cited work

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Fast r-cnn

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Momentum contrast for unsupervised visual representation learning

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This paper cites Image-text Retrieval: A Survey on Recent Research and Development.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Image-text Retrieval: A Survey on Recent Research and Development

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This paper cites Clip-guided vision-language pre-training for question answering in 3d scenes.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Clip-guided vision-language pre-training for question answering in 3d scenes

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Explaining and Harnessing Adversarial Examples

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This paper cites Adversarial training for free!Advances in neural information processing systems, 32, 2019.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Adversarial training for free!Advances in neural information processing systems, 32, 2019

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This paper cites On evaluating adversarial robustness of large vision-language models.Advances in Neural Information Processing Systems, 36:54111–54138, 2023.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting On evaluating adversarial robustness of large vision-language models.Advances in Neural Information Processing Systems, 36:54111–54138, 2023

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Theoretically principled trade-off between robustness and accuracy

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This paper cites Understanding Zero-Shot Adversarial Robustness for Large-Scale Models.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Exploring Visual Prompts for Adapting Large-Scale Models

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This paper cites Pre-trained model guided fine-tuning for zero-shot adversarial robustness.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Pre-trained model guided fine-tuning for zero-shot adversarial robustness

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This paper cites Text-guided attention is all you need for zero-shot robustness in vision-language models.Advances in Neural Information Processing Systems, 37: 96424–96448, 2024.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Text-guided attention is all you need for zero-shot robustness in vision-language models.Advances in Neural Information Processing Systems, 37: 96424–96448, 2024

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Towards Deep Learning Models Resistant to Adversarial Attacks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Towards evaluating the robustness of neural networks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Improving adversarial robustness by putting more regularizations on less robust samples

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Deepfool: a simple and accurate method to fool deep neural networks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Intriguing properties of neural networks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Adversarial examples in the physical world

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Explainable ai: A review of machine learning interpretability methods.Entropy, 23(1):18, 2020

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Security analysis and enhancement of model compressed deep learning systems under adversarial attacks

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Stochastic Activation Pruning for Robust Adversarial Defense

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Towards Deep Neural Network Architectures Robust to Adversarial Examples

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Adversarial examples are not bugs, they are features.Advances in neural information processing systems, 32, 2019

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Improving adversarial robustness requires revisiting misclassified examples

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Reducing excessive margin to achieve a bet- ter accuracy vs

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Attention is all you need.Advances in neural information processing systems, 30, 2017

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Bert: Pre-training of deep bidirectional transformers for language understanding

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Scaling up visual and vision-language representation learning with noisy text supervision

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Language-driven anchors for zero-shot adversarial robustness

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Improving zero-shot adversarial robustness in vision-language models by closed-form alignment of adversarial path simplices

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Imagenet: A large- scale hierarchical image database

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Learning multiple layers of features from tiny images

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting An analysis of single-layer networks in unsuper- vised feature learning

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting One-shot learning of object categories.IEEE transactions on pattern analysis and machine intelligence, 28(4):594–611, 2006

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Caltech-256 object category dataset

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Unresolved cited work

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Automated flower classification over a large number of classes

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Fine-Grained Visual Classification of Aircraft

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting 3d object representations for fine- grained categorization

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Sun database: Large-scale scene recognition from abbey to zoo

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Food-101–mining discriminative components with random forests

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Unresolved cited work

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Describing textures in the wild

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Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Limitations

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This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Improving Adversarial Robustness of Zero-Shot CLIP with Confidence-Aware Weighting Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

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