87 research outputs found
Learning Local Metrics and Influential Regions for Classification
The performance of distance-based classifiers heavily depends on the
underlying distance metric, so it is valuable to learn a suitable metric from
the data. To address the problem of multimodality, it is desirable to learn
local metrics. In this short paper, we define a new intuitive distance with
local metrics and influential regions, and subsequently propose a novel local
metric learning method for distance-based classification. Our key intuition is
to partition the metric space into influential regions and a background region,
and then regulate the effectiveness of each local metric to be within the
related influential regions. We learn local metrics and influential regions to
reduce the empirical hinge loss, and regularize the parameters on the basis of
a resultant learning bound. Encouraging experimental results are obtained from
various public and popular data sets
Dynamic Face Video Segmentation via Reinforcement Learning
For real-time semantic video segmentation, most recent works utilised a
dynamic framework with a key scheduler to make online key/non-key decisions.
Some works used a fixed key scheduling policy, while others proposed adaptive
key scheduling methods based on heuristic strategies, both of which may lead to
suboptimal global performance. To overcome this limitation, we model the online
key decision process in dynamic video segmentation as a deep reinforcement
learning problem and learn an efficient and effective scheduling policy from
expert information about decision history and from the process of maximising
global return. Moreover, we study the application of dynamic video segmentation
on face videos, a field that has not been investigated before. By evaluating on
the 300VW dataset, we show that the performance of our reinforcement key
scheduler outperforms that of various baselines in terms of both effective key
selections and running speed. Further results on the Cityscapes dataset
demonstrate that our proposed method can also generalise to other scenarios. To
the best of our knowledge, this is the first work to use reinforcement learning
for online key-frame decision in dynamic video segmentation, and also the first
work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at:
https://github.com/mapleandfire/300VW-Mas
Toward Certified Robustness of Distance Metric Learning
Metric learning aims to learn a distance metric such that semantically
similar instances are pulled together while dissimilar instances are pushed
away. Many existing methods consider maximizing or at least constraining a
distance margin in the feature space that separates similar and dissimilar
pairs of instances to guarantee their generalization ability. In this paper, we
advocate imposing an adversarial margin in the input space so as to improve the
generalization and robustness of metric learning algorithms. We first show
that, the adversarial margin, defined as the distance between training
instances and their closest adversarial examples in the input space, takes
account of both the distance margin in the feature space and the correlation
between the metric and triplet constraints. Next, to enhance robustness to
instance perturbation, we propose to enlarge the adversarial margin through
minimizing a derived novel loss function termed the perturbation loss. The
proposed loss can be viewed as a data-dependent regularizer and easily plugged
into any existing metric learning methods. Finally, we show that the enlarged
margin is beneficial to the generalization ability by using the theoretical
technique of algorithmic robustness. Experimental results on 16 datasets
demonstrate the superiority of the proposed method over existing
state-of-the-art methods in both discrimination accuracy and robustness against
possible noise
Learning local metrics and influential regions for classification
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem of multimodality, it is desirable to learn local metrics. In this short paper, we define a new intuitive distance with local metrics and influential regions, and subsequently propose a novel local metric learning algorithm called LMLIR for distance-based classification. Our key intuition is to partition the metric space into influential regions and a background region, and then regulate the effectiveness of each local metric to be within the related influential regions. We learn multiple local metrics and influential regions to reduce the empirical hinge loss, and regularize the parameters on the basis of a resultant learning bound. Encouraging experimental results are obtained from various public and popular data sets
Transferring CNNs to Multi-instance Multi-label Classification on Small Datasets
Image tagging is a well known challenge in image processing. It is typically addressed through multi-instance multi-label (MIML) classification methodologies. Convolutional Neural Networks (CNNs) possess great potential to perform well on MIML tasks, since multi-level convolution and max pooling coincide with the multi-instance setting and the sharing of hidden representation may benefit multi-label modeling. However, CNNs usually require a large amount of carefully labeled data for training, which is hard to obtain in many real applications. In this paper, we propose a new approach for transferring pre-trained deep networks such as VGG16 on Imagenet to small MIML tasks. We extract features from each group of the network layers and apply multiple binary classifiers to them for multi-label prediction. Moreover, we adopt an L1-norm regularized Logistic Regression (L1LR) to find the most effective features for learning the multi-label classifiers. The experiment results on two most-widely used and relatively small benchmark MIML image datasets demonstrate that the proposed approach can substantially outperform the state-of-the-art algorithms, in terms of all popular performance metrics
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice
Active learning aims to reduce annotation cost by predicting which samples
are useful for a human teacher to label. However it has become clear there is
no best active learning algorithm. Inspired by various philosophies about what
constitutes a good criteria, different algorithms perform well on different
datasets. This has motivated research into ensembles of active learners that
learn what constitutes a good criteria in a given scenario, typically via
multi-armed bandit algorithms. Though algorithm ensembles can lead to better
results, they overlook the fact that not only does algorithm efficacy vary
across datasets, but also during a single active learning session. That is, the
best criteria is non-stationary. This breaks existing algorithms' guarantees
and hampers their performance in practice. In this paper, we propose dynamic
ensemble active learning as a more general and promising research direction. We
develop a dynamic ensemble active learner based on a non-stationary multi-armed
bandit with expert advice algorithm. Our dynamic ensemble selects the right
criteria at each step of active learning. It has theoretical guarantees, and
shows encouraging results on popular datasets.Comment: This work has been accepted at ICPR2018 and won Piero Zamperoni Best
Student Paper Awar
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