delve
cleverhans
| delve | cleverhans | |
|---|---|---|
| 1 | 3 | |
| 77 | 6,079 | |
| - | 1.2% | |
| 4.0 | 0.0 | |
| about 1 year ago | 22 days ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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delve
cleverhans
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Clever Hans (Intelligence Misatributon)
I only knew of this story from looking up the name of this library on adversarial DL https://github.com/cleverhans-lab/cleverhans
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[D] Does anyone care about adversarial attacks anymore?
I feel as though this area has not received much attention over the last couple of years. The CleverHans project has gone stale and I haven't heard of many new results recently. Has the community lost interest in this area? Did we decide that adversarial attacks aren't such a problem in practical applications?
What are some alternatives?
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
pytea - PyTea: PyTorch Tensor shape error analyzer
advertorch - A Toolbox for Adversarial Robustness Research
explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
AIX360 - Interpretability and explainability of data and machine learning models
TorchDrift - Drift Detection for your PyTorch Models
aws-security-workshops - A collection of the latest AWS Security workshops
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks