3 research outputs found
Adversarial Partial Multi-Label Learning
Partial multi-label learning (PML), which tackles the problem of learning
multi-label prediction models from instances with overcomplete noisy
annotations, has recently started gaining attention from the research
community. In this paper, we propose a novel adversarial learning model,
PML-GAN, under a generalized encoder-decoder framework for partial multi-label
learning. The PML-GAN model uses a disambiguation network to identify noisy
labels and uses a multi-label prediction network to map the training instances
to the disambiguated label vectors, while deploying a generative adversarial
network as an inverse mapping from label vectors to data samples in the input
feature space. The learning of the overall model corresponds to a minimax
adversarial game, which enhances the correspondence of input features with the
output labels in a bi-directional mapping. Extensive experiments are conducted
on multiple datasets, while the proposed model demonstrates the
state-of-the-art performance for partial multi-label learning
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
Weakly supervised learning aims at coping with scarce labeled data. Previous
weakly supervised studies typically assume that there is only one kind of weak
supervision in data. In many applications, however, raw data usually contains
more than one kind of weak supervision at the same time. For example, in user
experience enhancement from Didi, one of the largest online ride-sharing
platforms, the ride comment data contains severe label noise (due to the
subjective factors of passengers) and severe label distribution bias (due to
the sampling bias). We call such a problem as "compound weakly supervised
learning". In this paper, we propose the CWSL method to address this problem
based on Didi ride-sharing comment data. Specifically, an instance reweighting
strategy is employed to cope with severe label noise in comment data, where the
weights for harmful noisy instances are small. Robust criteria like AUC rather
than accuracy and the validation performance are optimized for the correction
of biased data label. Alternating optimization and stochastic gradient methods
accelerate the optimization on large-scale data. Experiments on Didi
ride-sharing comment data clearly validate the effectiveness. We hope this work
may shed some light on applying weakly supervised learning to complex real
situations.Comment: AAAI 202
A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment
An infrastructure for multisite, geographically-distributed creation and
collection of diverse, high-quality, curated and labeled radiology image data
is crucial for the successful automated development, deployment, monitoring and
continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML)
solutions in the real world. An interactive radiology reporting approach that
integrates image viewing, dictation, natural language processing (NLP) and
creation of hyperlinks between image findings and the report, provides
localized labels during routine interpretation. These images and labels can be
captured and centralized in a cloud-based system. This method provides a
practical and efficient mechanism with which to monitor algorithm performance.
It also supplies feedback for iterative development and quality improvement of
new and existing algorithmic models. Both feedback and monitoring are achieved
without burdening the radiologist. The method addresses proposed regulatory
requirements for post-marketing surveillance and external data. Comprehensive
multi-site data collection assists in reducing bias. Resource requirements are
greatly reduced compared to dedicated retrospective expert labeling.Comment: 21 pages, 3 figure