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Found 9 papers, 7 papers with code
A Simple and Efficient Baseline for Data Attribution on Images
Our approach serves as a simple and efficient baseline for data attribution on images.
Understanding and Mitigating Copying in Diffusion Models
While it is widely believed that duplicated images in the training set are responsible for content replication at inference time, we observe that the text conditioning of the model plays a similarly important role.
What Can We Learn from Unlearnable Datasets?
First, it is widely believed that neural networks trained on unlearnable datasets only learn shortcuts, simpler rules that are not useful for generalization.
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes.
Autoregressive Perturbations for Data Poisoning
Unfortunately, existing methods require knowledge of both the target architecture and the complete dataset so that a surrogate network can be trained, the parameters of which are used to generate the attack.
Poisons that are learned faster are more effective
We advocate for evaluating poisons in terms of peak test accuracy.
Shift Invariance Can Reduce Adversarial Robustness
Using this, we prove that shift invariance in neural networks produces adversarial examples for the simple case of two classes, each consisting of a single image with a black or white dot on a gray background.
Low Curvature Activations Reduce Overfitting in Adversarial Training
In particular, we show that using activation functions with low (exact or approximate) curvature values has a regularization effect that significantly reduces both the standard and robust generalization gaps in adversarial training.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
