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Found 18 papers, 2 papers with code
Towards Understanding Dual BN In Hybrid Adversarial Training
There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples (termed Hybrid-AT).
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
In this work, we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness.
Robustness of SAM: Segment Anything Under Corruptions and Beyond
Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption.
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt.
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentation.
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples
Beyond the basic goal of mask removal, we further investigate and find that it is possible to generate any desired mask by the adversarial attack.
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey.
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI
This work conducts a survey on audio diffusion model, which is complementary to existing surveys that either lack the recent progress of diffusion-based speech synthesis or highlight an overall picture of applying diffusion model in multiple fields.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
Text-to-image Diffusion Models in Generative AI: A Survey
As a self-contained work, this survey starts with a brief introduction of how diffusion models work for image synthesis, followed by the background for text-conditioned image synthesis.
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond
Masked autoencoders are scalable vision learners, as the title of MAE \cite{he2022masked}, which suggests that self-supervised learning (SSL) in vision might undertake a similar trajectory as in NLP.
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields.
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
This yields a unified perspective on how negative samples and SimSiam alleviate collapse.
Fast Adversarial Training with Noise Augmentation: A Unified Perspective on RandStart and GradAlign
The former works only for a relatively small perturbation 8/255 with the l_\infty constraint, and GradAlign improves it by extending the perturbation size to 16/255 (with the l_\infty constraint) but at the cost of being 3 to 4 times slower.
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL
Towards avoiding collapse in self-supervised learning (SSL), contrastive loss is widely used but often requires a large number of negative samples.
Early Stop And Adversarial Training Yield Better surrogate Model: Very Non-Robust Features Harm Adversarial Transferability
The transferability of adversarial examples (AE); known as adversarial transferability, has attracted significant attention because it can be exploited for TransferableBlack-box Attacks (TBA).
EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search
In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.
