Database Paper Browser

Back to papers

Adaptive Data Augmentation for Supervised Learning over Missing Data

Summary: Proposes an unsupervised DAGAN, a GAN framework for adaptive data augmentation to bridge missing-value patterns across source and target data in supervised learning. Dual-GANs learn target noise masks and augment source data before retraining, boosting robustness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12311
Venue
VLDB
Year
2021
Pagerank
5.7506746e-05
Overall Rank
5,028 | 65.03%
DOI
10.14778/3450980.3450989

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers