cleverhans
pytea
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| cleverhans | pytea | |
|---|---|---|
| 3 | 3 | |
| 6,008 | 310 | |
| 0.0% | 0.3% | |
| 0.0 | 1.8 | |
| about 1 year ago | almost 2 years ago | |
| Jupyter Notebook | TypeScript | |
| MIT License | GNU General Public License v3.0 or later |
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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?
pytea
What are some alternatives?
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.
examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
advertorch - A Toolbox for Adversarial Robustness Research
uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
AIX360 - Interpretability and explainability of data and machine learning models
WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
aws-security-workshops - A collection of the latest AWS Security workshops
vivit - [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps
Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
TorchDrift - Drift Detection for your PyTorch Models
delve - PyTorch model training and layer saturation monitor