Search Results for author:
Found 4 papers, 2 papers with code
DeDrift: Robust Similarity Search under Content Drift
We investigate the impact of this "content drift" for large-scale similarity search tools, based on nearest neighbor search in embedding space.
Similarity Search for Efficient Active Learning and Search of Rare Concepts
Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples.
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Label noise is increasingly prevalent in datasets acquired from noisy channels.
Ranked #22 on
Image Classification
on Clothing1M
(using extra training data)
Billion-scale semi-supervised learning for image classification
This paper presents a study of semi-supervised learning with large convolutional networks.
Ranked #6 on
Image Classification
on OmniBenchmark
(using extra training data)
