{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/m-darts-model-uncertainty-aware","title":"$μ$DARTS: Model Uncertainty-Aware Differentiable Architecture Search","arxiv_id":"2107.11500","date":"2021-07-24","proceeding":null,"authors":["Biswadeep Chakraborty","Saibal Mukhopadhyay"],"abstract":"We present a Model Uncertainty-aware Differentiable ARchiTecture Search ($\\mu$DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We introduce concrete dropout within DARTS cells and include a Monte-Carlo regularizer within the training loss to optimize the concrete dropout probabilities. A predictive variance term is introduced in the validation loss to enable searching for architecture with minimal model uncertainty. The experiments on CIFAR10, CIFAR100, SVHN, and ImageNet verify the effectiveness of $\\mu$DARTS in improving accuracy and reducing uncertainty compared to existing DARTS methods. Moreover, the final architecture obtained from $\\mu$DARTS shows higher robustness to noise at the input image and model parameters compared to the architecture obtained from existing DARTS methods.","url_abs":"https://arxiv.org/abs/2107.11500v2","url_pdf":"https://arxiv.org/pdf/2107.11500v2.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":[],"tasks":[{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"concrete-dropout","method_name":"Concrete Dropout"},{"method_slug":"darts","method_name":"DARTS"},{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/neural-architecture-search-on-cifar-10","task":"Neural Architecture Search","dataset":"CIFAR-10","model":"μDARTS","rank_in_archive_order":37,"of":41,"metrics":{"FLOPS":"602M","Search Time (GPU days)":"0.1","Top-1 Error Rate":"3.277%"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-cifar-100-1","task":"Neural Architecture Search","dataset":"CIFAR-100","model":"μDARTS","rank_in_archive_order":12,"of":13,"metrics":{"PARAMS":"602M","Percentage Error":"19.39","Search Time (GPU days)":"1.57"},"uses_additional_data":false},{"leaderboard":"/sota/neural-architecture-search-on-imagenet","task":"Neural Architecture Search","dataset":"ImageNet","model":"μDARTS","rank_in_archive_order":43,"of":135,"metrics":{"Accuracy":"78.76","Params":"602M","Top-1 Error Rate":"21.24"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}