18,826 research outputs found
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
Self-Supervised Learning (SSL) has emerged as the solution of choice to learn
transferable representations from unlabeled data. However, SSL requires to
build samples that are known to be semantically akin, i.e. positive views.
Requiring such knowledge is the main limitation of SSL and is often tackled by
ad-hoc strategies e.g. applying known data-augmentations to the same input. In
this work, we generalize and formalize this principle through Positive Active
Learning (PAL) where an oracle queries semantic relationships between samples.
PAL achieves three main objectives. First, it unveils a theoretically grounded
learning framework beyond SSL, that can be extended to tackle supervised and
semi-supervised learning depending on the employed oracle. Second, it provides
a consistent algorithm to embed a priori knowledge, e.g. some observed labels,
into any SSL losses without any change in the training pipeline. Third, it
provides a proper active learning framework yielding low-cost solutions to
annotate datasets, arguably bringing the gap between theory and practice of
active learning that is based on simple-to-answer-by-non-experts queries of
semantic relationships between inputs.Comment: 8 main pages, 20 totals, 10 figure
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions
Labeling social-media data for custom dimensions of toxicity and social bias
is challenging and labor-intensive. Existing transfer and active learning
approaches meant to reduce annotation effort require fine-tuning, which suffers
from over-fitting to noise and can cause domain shift with small sample sizes.
In this work, we propose a novel Active Transfer Few-shot Instructions (ATF)
approach which requires no fine-tuning. ATF leverages the internal linguistic
knowledge of pre-trained language models (PLMs) to facilitate the transfer of
information from existing pre-labeled datasets (source-domain task) with
minimum labeling effort on unlabeled target data (target-domain task). Our
strategy can yield positive transfer achieving a mean AUC gain of 10.5%
compared to no transfer with a large 22b parameter PLM. We further show that
annotation of just a few target-domain samples via active learning can be
beneficial for transfer, but the impact diminishes with more annotation effort
(26% drop in gain between 100 and 2000 annotated examples). Finally, we find
that not all transfer scenarios yield a positive gain, which seems related to
the PLMs initial performance on the target-domain task.Comment: Accepted to NeurIPS Workshop on Transfer Learning for Natural
Language Processing, 2022, New Orlean
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Active learning of an action detector on untrimmed videos
textCollecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple actions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to localize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that annotating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more efficiently than alternative active learning strategies that fail to accommodate the "untrimmed" nature of real video data.Computer Science
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