18 research outputs found
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Active learning (AL) combines data labeling and model training to minimize
the labeling cost by prioritizing the selection of high value data that can
best improve model performance. In pool-based active learning, accessible
unlabeled data are not used for model training in most conventional methods.
Here, we propose to unify unlabeled sample selection and model training towards
minimizing labeling cost, and make two contributions towards that end. First,
we exploit both labeled and unlabeled data using semi-supervised learning (SSL)
to distill information from unlabeled data during the training stage. Second,
we propose a consistency-based sample selection metric that is coherent with
the training objective such that the selected samples are effective at
improving model performance. We conduct extensive experiments on image
classification tasks. The experimental results on CIFAR-10, CIFAR-100 and
ImageNet demonstrate the superior performance of our proposed method with
limited labeled data, compared to the existing methods and the alternative AL
and SSL combinations. Additionally, we study an important yet under-explored
problem -- "When can we start learning-based AL selection?". We propose a
measure that is empirically correlated with the AL target loss and is
potentially useful for determining the proper starting point of learning-based
AL methods.Comment: Accepted by ECCV202
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
In this paper, we address the problem of training deep neural networks in the
presence of severe label noise. Our proposed training algorithm ScanMix,
combines semantic clustering with semi-supervised learning (SSL) to improve the
feature representations and enable an accurate identification of noisy samples,
even in severe label noise scenarios. To be specific, ScanMix is designed based
on the expectation maximisation (EM) framework, where the E-step estimates the
value of a latent variable to cluster the training images based on their
appearance representations and classification results, and the M-step optimises
the SSL classification and learns effective feature representations via
semantic clustering. In our evaluations, we show state-of-the-art results on
standard benchmarks for symmetric, asymmetric and semantic label noise on
CIFAR-10 and CIFAR-100, as well as large scale real label noise on WebVision.
Most notably, for the benchmarks contaminated with large noise rates (80% and
above), our results are up to 27% better than the related work. The code is
available at https://github.com/ragavsachdeva/ScanMix
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Deep neural network models are robust to a limited amount of label noise, but
their ability to memorise noisy labels in high noise rate problems is still an
open issue. The most competitive noisy-label learning algorithms rely on a
2-stage process comprising an unsupervised learning to classify training
samples as clean or noisy, followed by a semi-supervised learning that
minimises the empirical vicinal risk (EVR) using a labelled set formed by
samples classified as clean, and an unlabelled set with samples classified as
noisy. In this paper, we hypothesise that the generalisation of such 2-stage
noisy-label learning methods depends on the precision of the unsupervised
classifier and the size of the training set to minimise the EVR. We empirically
validate these two hypotheses and propose the new 2-stage noisy-label training
algorithm LongReMix. We test LongReMix on the noisy-label benchmarks CIFAR-10,
CIFAR-100, WebVision, Clothing1M, and Food101-N. The results show that our
LongReMix generalises better than competing approaches, particularly in high
label noise problems. Furthermore, our approach achieves state-of-the-art
performance in most datasets. The code will be available upon paper acceptance