Methods › General › Semi-Supervised Learning Methods › STAC
STAC
Introduced by Kihyuk Sohn et al. in A Simple Semi-Supervised Learning Framework for Object Detection
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
STAC is a semi-supervised framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations. We generate pseudo labels (i.e., bounding boxes and their class labels) for unlabeled data using test-time inference, including NMS , of the teacher model trained with labeled data. We then compute unsupervised loss with respect to pseudo labels whose confidence scores are above a threshold τ . The strong augmentations are applied for augmentation consistency during the model training. Target boxes are augmented when global geometric transformations are used.
Papers archive 2025-07-28
8 shown of 8, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation 18 Oct 2024 · 1 repository · arXiv:2410.14210
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STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics 27 Jan 2024 · 1 repository · arXiv:2401.15288
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Structured Dialogue Discourse Parsing 26 Jun 2023 · 1 repository · arXiv:2306.15103
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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis 16 Jun 2023 · 0 repositories · arXiv:2306.10128
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Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues 12 Feb 2023 · 0 repositories · arXiv:2302.05895
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DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation 28 Mar 2022 · 0 repositories · arXiv:2203.14481
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Humble Teachers Teach Better Students for Semi-Supervised Object Detection 19 Jun 2021 · 0 repositories · arXiv:2106.10456
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A Simple Semi-Supervised Learning Framework for Object Detection 10 May 2020 · 7 repositories · arXiv:2005.04757Syntology ran 0 of 1 samples · 1 unverified
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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