73,811 research outputs found
Semi-supervised Tuning from Temporal Coherence
Recent works demonstrated the usefulness of temporal coherence to regularize
supervised training or to learn invariant features with deep architectures. In
particular, enforcing smooth output changes while presenting temporally-closed
frames from video sequences, proved to be an effective strategy. In this paper
we prove the efficacy of temporal coherence for semi-supervised incremental
tuning. We show that a deep architecture, just mildly trained in a supervised
manner, can progressively improve its classification accuracy, if exposed to
video sequences of unlabeled data. The extent to which, in some cases, a
semi-supervised tuning allows to improve classification accuracy (approaching
the supervised one) is somewhat surprising. A number of control experiments
pointed out the fundamental role of temporal coherence.Comment: Under review as a conference paper at ICLR 201
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
A growing number of applications, e.g. video surveillance and medical image
analysis, require training recognition systems from large amounts of weakly
annotated data while some targeted interactions with a domain expert are
allowed to improve the training process. In such cases, active learning (AL)
can reduce labeling costs for training a classifier by querying the expert to
provide the labels of most informative instances. This paper focuses on AL
methods for instance classification problems in multiple instance learning
(MIL), where data is arranged into sets, called bags, that are weakly labeled.
Most AL methods focus on single instance learning problems. These methods are
not suitable for MIL problems because they cannot account for the bag structure
of data. In this paper, new methods for bag-level aggregation of instance
informativeness are proposed for multiple instance active learning (MIAL). The
\textit{aggregated informativeness} method identifies the most informative
instances based on classifier uncertainty, and queries bags incorporating the
most information. The other proposed method, called \textit{cluster-based
aggregative sampling}, clusters data hierarchically in the instance space. The
informativeness of instances is assessed by considering bag labels, inferred
instance labels, and the proportion of labels that remain to be discovered in
clusters. Both proposed methods significantly outperform reference methods in
extensive experiments using benchmark data from several application domains.
Results indicate that using an appropriate strategy to address MIAL problems
yields a significant reduction in the number of queries needed to achieve the
same level of performance as single instance AL methods
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