Methods › Computer Vision › Image Model Blocks › Elastic Dense Block

Elastic Dense Block

2 papers tagged archive 2025-07-28

Introduced by Huiyu Wang et al. in ELASTIC: Improving CNNs with Dynamic Scaling Policies

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

Elastic Dense Block is a skip connection block that modifies the Dense Block with downsamplings and upsamplings in parallel branches at each layer to let the network learn from a data scaling policy in which inputs are processed at different resolutions in each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.

PaperSourceSee Code · allenai/elastic

Papers archive 2025-07-28

2 shown of 2, 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

8 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
Attribute1
General Classification1
Image Classification1
MUlTI-LABEL-ClASSIFICATION1
Multi-Label Classification1
Object Detection1
Real-Time Object Detection1
Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with Elastic Dense Block: 2018 to 2019, peak 1 1 0 2018: 1 paper 2018 2019: 1 paper 2019
Papers per year the archive tags with this method, by the paper's archive date (2 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

Image Model BlocksSkip Connection Blocks

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