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Unlike recent robustness research, this benchmark\nevaluates performance on commonplace corruptions not worst-case adversarial\ncorruptions. We find that there are negligible changes in relative corruption\nrobustness from AlexNet to ResNet classifiers, and we discover ways to enhance\ncorruption robustness. Then we propose a new dataset called Icons-50 which\nopens research on a new kind of robustness, surface variation robustness. With\nthis dataset we evaluate the frailty of classifiers on new styles of known\nobjects and unexpected instances of known classes. We also demonstrate two\nmethods that improve surface variation robustness. Together our benchmarks may\naid future work toward networks that learn fundamental class structure and also\nrobustly generalize.","url_abs":"http://arxiv.org/abs/1807.01697v5","url_pdf":"http://arxiv.org/pdf/1807.01697v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"benchmarking-neural-network-robustness-to","repo_url":"https://github.com/bethgelab/imagecorruptions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"benchmarking-neural-network-robustness-to","repo_url":"https://github.com/neurai-lab/dsp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"adversarial-defense","task_name":"Adversarial Defense"},{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"icons-50","name":"Icons-50","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.01697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.01697"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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