Methods › General › Regularization › DropConnect

DropConnect

84 papers tagged archive 2025-07-28

Introduced by Li Wan et al. in Regularization of Neural Networks using DropConnect

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

DropConnect generalizes Dropout by randomly dropping the weights rather than the activations with probability 1-p. DropConnect is similar to Dropout as it introduces dynamic sparsity within the model, but differs in that the sparsity is on the weights W, rather than the output vectors of a layer. In other words, the fully connected layer with DropConnect becomes a sparsely connected layer in which the connections are chosen at random during the training stage. Note that this is not equivalent to setting W to be a fixed sparse matrix during training.

For a DropConnect layer, the output is given as:

r = a ((M * W)v)

Here r is the output of a layer, v is the input to a layer, W are weight parameters, and M is a binary matrix encoding the connection information where Mᵢⱼ ∼Bernoulli(p). Each element of the mask M is drawn independently for each example during training, essentially instantiating a different connectivity for each example seen. Additionally, the biases are also masked out during training.

PaperSee Code · teelinsan/KerasDropconnect

Source in the archive: Regularization of Neural Networks using DropConnect, a link on cds.nyu.edu (archive link, not checked and not linked: not a paper host this site links to).

Papers archive 2025-07-28

30 shown of 84, 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 93 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
Language Modelling20
Language Modeling18
Transfer Learning18
Text Classification16
General Classification15
text-classification12
Classification11
Sentiment Analysis9
Image Classification8
Translation6
image-classification5
Language Identification4
Machine Translation4
Word Embeddings4
Bayesian Inference3
Decision Making3
Hate Speech Detection3
Sentence3
Sentiment Classification3
Time Series3

Usage over time archive 2025-07-28

Papers per year tagged with DropConnect: 2013 to 2025, peak 21 21 0 2013: 2 papers 2013 2014: 0 papers 2014 2015: 2 papers 2015 2016: 3 papers 2016 2017: 3 papers 2017 2018: 6 papers 2018 2019: 20 papers 2019 2020: 21 papers 2020 2021: 15 papers 2021 2022: 4 papers 2022 2023: 2 papers 2023 2024: 4 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (84 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

Regularization

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