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> ML_LITERATURE // HUANG-2017-DENSELY-CONNECTED-CONVOLUTIONAL-NETWORKS-DENSENET_v1.0

Densely Connected Convolutional Networks (DenseNet)

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger · IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

seminal-architecture2017industry-standardthirdPartyReproduced
DOI: 10.1109/CVPR.2017.243Back to Literature

Principal Contribution

Connected each layer to every other layer in a feed-forward fashion via channel concatenation, promoting feature reuse and alleviating vanishing gradients.

Operational Relevance

Serves as canonical technical reference for implementing task-image-classification, task-medical-imaging in production systems.

Assumptions

  • Underlying data distribution satisfies empirical consistency and regularity assumptions across training domains

Limitations

  • Scaling characteristics and accuracy depend on hardware architecture, parameter scale, and dataset quality

Connected Algorithms, Architectures & Tools

Related Algorithms:
Simple Framework for Contrastive Learning (SimCLR)
Related Architectures:
Implementing Libraries: