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Deep Layer Aggregation

DLA

94 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DLA, or Deep Layer Aggregation, iteratively and hierarchically merges the feature hierarchy across layers in neural networks to make networks with better accuracy and fewer parameters.

In iterative deep aggregation (IDA), aggregation begins at the shallowest, smallest scale and then iteratively merges deeper, larger scales. In this way shallow features are refined as they are propagated through different stages of aggregation.

In hierarchical deep aggregation (HDA), blocks and stages in a tree are merged to preserve and combine feature channels. With HDA shallower and deeper layers are combined to learn richer combinations that span more of the feature hierarchy. While IDA effectively combines stages, it is insufficient for fusing the many blocks of a network, as it is still only sequential.

Source: Deep Layer Aggregation

Papers archive 2025-07-28

30 shown of 94, 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.

Tasks archive 2025-07-28

20 shown of 136 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Detection38
object-detection34
Object21
Document Layout Analysis17
Object Tracking8
Optical Character Recognition (OCR)7
Semantic Segmentation6
Multi-Object Tracking5
Multiple Object Tracking5
Pose Estimation5
Image Classification4
Optical Character Recognition4
Retrieval4
Decoder3
Deep Learning3
Information Retrieval3
Instance Segmentation3
Keypoint Detection3
Knowledge Distillation3
Representation Learning3

Usage over time archive 2025-07-28

Papers per year tagged with DLA: 2017 to 2025, peak 19 19 0 2017: 1 paper 2017 2018: 2 papers 2018 2019: 8 papers 2019 2020: 9 papers 2020 2021: 17 papers 2021 2022: 14 papers 2022 2023: 18 papers 2023 2024: 19 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (94 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Feature Extractors

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