Papers › Distilling Knowledge via Knowledge Review

Distilling Knowledge via Knowledge Review

19 Apr 2021CVPR 2021 1arXiv:2104.09044archive 2025-07-28

Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia

Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We differently study the factor of connection path cross levels between teacher and student networks, and reveal its great importance. For the first time in knowledge distillation, cross-stage connection paths are proposed. Our new review mechanism is effective and structurally simple. Our finally designed nested and compact framework requires negligible computation overhead, and outperforms other methods on a variety of tasks. We apply our method to classification, object detection, and instance segmentation tasks. All of them witness significant student network performance improvement. Code is available at https://github.com/Jia-Research-Lab/ReviewKD

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Code

Syntology Ran 5 of 8 code samples harvested from 5 repositories linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 4 ran with no contract checked.

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Jia-Research-Lab/ReviewKD officialmentioned in papermentioned on GitHubpytorch report
DevPranjal/reproduction-review-kd mentioned on GitHubpytorch report
ZJCV/KnowledgeReview mentioned on GitHubpytorchApache-2.0 report
dvlab-research/reviewkd mentioned on GitHubpytorch report
ruipingl/skr_pea mentioned on GitHubpytorch report
ruipingl/transkd mentioned on GitHubpytorch report

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8 samples harvested; 5 ran; 0 honoured the contract we drafted; 3 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
4ran
3unverified

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ABF Jia-Research-Lab/ReviewKD/CIFAR-100/model/reviewkd.py official repository unverified no licence file found · pointer only · 8db7e32366ae454d · report
ReviewKD Jia-Research-Lab/ReviewKD/CIFAR-100/model/reviewkd.py official repository unverified no licence file found · pointer only · cc67bd1bd51e7d3b · report
ABF ruipingl/skr_pea/train/ReviewKD.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only · 6613345022d326fc · report
HierarchicalContextLoss yoshitomo-matsubara/torchdistill/torchdistill/losses/mid_level.py community (archive-listed) ran MIT (permissive) · 407f14bd274a60dd · report
RLF_for_Resnet DevPranjal/reproduction-review-kd/framework.py community (archive-listed) ran no licence file found · pointer only · 3bb723e9c090a1d0 · report
ReviewKD ruipingl/skr_pea/train/ReviewKD.py community (archive-listed) ran fingerprinted no licence file found · pointer only · 65da0d1fda7c81fe · report
register_mid_level_loss yoshitomo-matsubara/torchdistill/torchdistill/losses/mid_level.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 3daf804e7ed5c814 · report
hcl ZJCV/KnowledgeReview/rfd/criterion/rfd_loss.py community (archive-listed) unverified Apache-2.0 (permissive) · 7919e3e477475e60 · report

Tasks

Instance SegmentationKnowledge DistillationObject DetectionSemantic Segmentationobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Knowledge Distillation CIFAR-100 resnet8x4 (T: resnet32x4 S: resnet8x4) Top-1 Accuracy (%) 75.63 #14 of 27 Archive leaderboard report
Knowledge Distillation CIFAR-100 vgg8 (T:vgg13 S:vgg8) Top-1 Accuracy (%) 74.84 #17 of 27 Archive leaderboard report
Knowledge Distillation ImageNet Knowledge Review (T: ResNet-34 S:ResNet-18) CRD training setting ✓ #44 of 52 Archive leaderboard report
Knowledge Distillation ImageNet Knowledge Review (T: ResNet-34 S:ResNet-18) Top-1 accuracy % 71.61 #44 of 52 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.

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