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Found 10 papers, 5 papers with code
Rethinking Backdoor Attacks
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation.
FFCV: Accelerating Training by Removing Data Bottlenecks
For example, we are able to train an ImageNet ResNet-50 model to 75\% in only 20 mins on a single machine.
TRAK: Attributing Model Behavior at Scale
That is, computationally tractable methods can struggle with accurately attributing model predictions in non-convex settings (e. g., in the context of deep neural networks), while methods that are effective in such regimes require training thousands of models, which makes them impractical for large models or datasets.
Raising the Cost of Malicious AI-Powered Image Editing
We present an approach to mitigating the risks of malicious image editing posed by large diffusion models.
Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Visual systems of primates are the gold standard of robust perception.
Datamodels: Predicting Predictions from Training Data
We present a conceptual framework, datamodeling, for analyzing the behavior of a model class in terms of the training data.
3DB: A Framework for Debugging Computer Vision Models
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
Revisiting Ensembles in an Adversarial Context: Improving Natural Accuracy
A necessary characteristic for the deployment of deep learning models in real world applications is resistance to small adversarial perturbations while maintaining accuracy on non-malicious inputs.
The Two Regimes of Deep Network Training
Learning rate schedule has a major impact on the performance of deep learning models.
Smallify: Learning Network Size while Training
As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy.
