Foundational Robustness of Foundation Models (NeurIPS 2022 tutorial)
This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models by Pin-Yu Chen (IBM Research), Sijia Liu (Michigan State University), and Sayak Paul (Carted).
Schedule
TBA
Outline
TBA
Navigating the codebase
We provide code for analytical tools for two types of models: vision and code. Below provides a high-level
overview of what code
and vision_models
directories contain:
vision_models
├── probing_transformer_models
│ ├── attention_distance
│ ├── attention_maps
│ ├── linear_projections
│ └── positional_embeddings
├── representation_effectiveness
│ ├── fourier_heatmap
│ ├── masking
│ ├── pgd_attacks
│ └── spectral_decomposition
└── robustness_eval
code
(TBA)
Each directory provides a standalone README.md
with instructions about executing the
scripts / notebooks.