Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-aug…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
7 results for “mixmatch”
MixMOOD improves SSDL by selecting unlabelled data based on deep feature similarity.
problem Class distribution mismatch in semi-supervised learning.
method MixMOOD uses deep dataset dissimilarity measures to select unlabelled data.
result MixMOOD selects unlabelled data based on strong correlation with MixMatch accuracy.
Improved accuracy with fewer labels using MixMatch and active learning.
problem Achieving high accuracy with limited labeled data.
method Combining MixMatch and active learning techniques.
result Significant improvement in accuracy on CIFAR-10, CIFAR-100, and SVHN datasets.
NS3L improves SSL algorithms by adding negative sampling, achieving better results.
problem Improving semi-supervised learning performance.
method Negative Sampling in Semi-Supervised Learning (NS3L) algorithm.
result NS3L significantly improves SSL algorithms like VAT and MixMatch.
ReMixMatch improves semi-supervised learning with new techniques for data efficiency.
problem Improving semi-supervised learning with limited labeled data.
method Distribution alignment and augmentation anchoring with AutoAugment.
result Significantly more data-efficient, requiring less labeled data for similar accuracy.
Semi-supervised learning improves with partial label information.
problem Improving model performance with limited labeled data.
method Contrastive learning with partial label information to encourage same labels.
result Partial label information reduces test error by up to 5.5 times.
TransMatch uses transfer learning to improve few-shot learning accuracy.
problem Building robust models with limited labeled data.
method Transfer-learning framework combining feature extraction, initialization, and semi-supervised learning.
result Significant improvement in few-shot learning accuracy.