Data Augmentation Can Improve Robustness
Authors: Sylvestre-Alvise Rebuffi, Sven Gowal, Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Timothy A Mann
NeurIPS 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on CIFAR-10 against ℓ and ℓ2 norm-bounded perturbations of size ϵ = 8/255 and ϵ = 128/255, respectively. We show large absolute improvements of +2.93% and +2.16% in robust accuracy compared to previous state-of-the-art methods. We conduct thorough experiments to show that our approach generalizes across architectures, datasets and threat models. |
| Researcher Affiliation | Industry | Sylvestre-Alvise Rebuffi*, Sven Gowal*, Dan Calian, Florian Stimberg, Olivia Wiles and Timothy Mann Deep Mind, London EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code written in JAX [4] and Haiku [26] is available online at https://github.com/ deepmind/deepmind-research/tree/master/adversarial_robustness. |
| Open Datasets | Yes | We evaluate our approach on CIFAR-10 against ℓ and ℓ2 norm-bounded perturbations of size ϵ = 8/255 and ϵ = 128/255, respectively. We also achieve a significant performance boost with this approach while using other architectures and datasets such as CIFAR-100, SVHN and TINYIMAGENET. |
| Dataset Splits | Yes | Specifically, we train two (and only two) models for each hyperparameter setting, perform early stopping for each model on a separate validation set of 1024 samples using PGD40 similarly to Rice et al. [44] and pick the best model by evaluating the robust accuracy on the same validation set . |
| Hardware Specification | Yes | We train for 400 epochs with a batch size of 512 split over 32 Google Cloud TPU v3 cores [4], and the learning rate is initially set to 0.1 and decayed by a factor 10 two-thirds-of-the-way through training. |
| Software Dependencies | No | The paper mentions JAX [4] and Haiku [26] but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We train for 400 epochs with a batch size of 512 split over 32 Google Cloud TPU v3 cores [4], and the learning rate is initially set to 0.1 and decayed by a factor 10 two-thirds-of-the-way through training. We scale the learning rates using the linear scaling rule of Goyal et al. [21] (i.e., effective LR = max(LR batch size/256, LR)). The decay rate of WA is set to τ = 0.999. |