Papers › What's Hidden in a Randomly Weighted Neural Network?

What's Hidden in a Randomly Weighted Neural Network?

29 Nov 2019CVPR 2020 6arXiv:1911.13299archive 2025-07-28

Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari

Training a neural network is synonymous with learning the values of the weights. By contrast, we demonstrate that randomly weighted neural networks contain subnetworks which achieve impressive performance without ever training the weight values. Hidden in a randomly weighted Wide ResNet-50 we show that there is a subnetwork (with random weights) that is smaller than, but matches the performance of a ResNet-34 trained on ImageNet. Not only do these "untrained subnetworks" exist, but we provide an algorithm to effectively find them. We empirically show that as randomly weighted neural networks with fixed weights grow wider and deeper, an "untrained subnetwork" approaches a network with learned weights in accuracy. Our code and pretrained models are available at https://github.com/allenai/hidden-networks.

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Code

Syntology Ran 2 of 9 code samples harvested from 2 repositories linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran with no contract checked.

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allenai/hidden-networks officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
julianscher/SLTN-GA mentioned on GitHubpytorchApache-2.0 report
kosnil/signed_supermasks mentioned on GitHubtf report
x-zho14/hidden-networks mentioned on GitHubpytorchApache-2.0 report

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1ran · our draft was wrong
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accumulate allenai/hidden-networks/utils/net_utils.py official repository unverified Apache-2.0 (permissive) · 8a42a6951b0c5e57 · report
arg_to_varname allenai/hidden-networks/configs/parser.py official repository unverified Apache-2.0 (permissive) · 177a4abf40724f6e · report
argv_to_vars allenai/hidden-networks/configs/parser.py official repository unverified Apache-2.0 (permissive) · ffc9d7cced6f71fb · report
get_lr allenai/hidden-networks/utils/net_utils.py official repository unverified Apache-2.0 (permissive) · 717f5458343a0b63 · report
trim_preceding_hyphens allenai/hidden-networks/configs/parser.py official repository unverified Apache-2.0 (permissive) · a99f170ebd682af5 · report
count_trainable_parameters julianscher/SLTN-GA/utilities/net_utils.py community (archive-listed) ran Apache-2.0 (permissive) · a3826392847cbef3 · report
get_all_parameters julianscher/SLTN-GA/utilities/net_utils.py community (archive-listed) unverified Apache-2.0 (permissive) · e9801080cb0de319 · report
get_number_of_parameters julianscher/SLTN-GA/utilities/net_utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 3d02b4b4c0045263 · report
parse_config_file identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 520a6cd6bfeb5831 · report

Tasks

Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet Wide ResNet-50 (edge-popup) Number of params 20.6M #988 of 1060 Archive leaderboard report
Image Classification ImageNet Wide ResNet-50 (edge-popup) Top 1 Accuracy 73.3% #988 of 1060 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 ConvolutionAdamAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionCosine AnnealingDropoutGlobal Average PoolingKaiming InitializationMax PoolingReLUResidual BlockResidual ConnectionSGD with MomentumWeight DecayWide Residual BlockWideResNet

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