/robustness-foundation-models

This repository holds code and other relevant files for the NeurIPS 2022 tutorial: Foundational Robustness of Foundation Models.

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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.