Robustness between the worst and average case

Last update: Dec 02, 2022

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Overview

Robustness between the worst and average case

A repository that implements intermediate robustness training and evaluation from the NeurIPS 2021 paper Robustness between the worst and average case. Created by Leslie Rice, Anna Bair, Huan Zhang and Zico Kolter.

Installation and usage

To install all required packages run: pip install -r requirements.txt.
Pretrained model weights can be downloaded here.
To train (l_infty perturbations), run python train.py -c {path_to_training_config_file}.json.
To evaluate (l_infty perturbations), run python train.py -c {path_to_evaluation_config_file}.json.
To train (spatial transformations), run python train_discrete.py -c {path_to_training_config_file}.json.
To evaluate (spatial transformations), run python eval_discrete.py --checkpoint {path_to_model_checkpoint}.pth.

Owner

CMU Locus Lab

Zico Kolter's Research Group

GitHub Repository

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Robustness between the worst and average case

Related tags

Overview

Robustness between the worst and average case

Installation and usage

Owner

CMU Locus Lab

Pytorch implementation of ProjectedGAN

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

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Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

Intrinsic Image Harmonization

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