5,276 research outputs found
Robust Representation Learning for Unreliable Partial Label Learning
Partial Label Learning (PLL) is a type of weakly supervised learning where
each training instance is assigned a set of candidate labels, but only one
label is the ground-truth. However, this idealistic assumption may not always
hold due to potential annotation inaccuracies, meaning the ground-truth may not
be present in the candidate label set. This is known as Unreliable Partial
Label Learning (UPLL) that introduces an additional complexity due to the
inherent unreliability and ambiguity of partial labels, often resulting in a
sub-optimal performance with existing methods. To address this challenge, we
propose the Unreliability-Robust Representation Learning framework (URRL) that
leverages unreliability-robust contrastive learning to help the model fortify
against unreliable partial labels effectively. Concurrently, we propose a dual
strategy that combines KNN-based candidate label set correction and
consistency-regularization-based label disambiguation to refine label quality
and enhance the ability of representation learning within the URRL framework.
Extensive experiments demonstrate that the proposed method outperforms
state-of-the-art PLL methods on various datasets with diverse degrees of
unreliability and ambiguity. Furthermore, we provide a theoretical analysis of
our approach from the perspective of the expectation maximization (EM)
algorithm. Upon acceptance, we pledge to make the code publicly accessible
Active Discovery of Network Roles for Predicting the Classes of Network Nodes
Nodes in real world networks often have class labels, or underlying
attributes, that are related to the way in which they connect to other nodes.
Sometimes this relationship is simple, for instance nodes of the same class are
may be more likely to be connected. In other cases, however, this is not true,
and the way that nodes link in a network exhibits a different, more complex
relationship to their attributes. Here, we consider networks in which we know
how the nodes are connected, but we do not know the class labels of the nodes
or how class labels relate to the network links. We wish to identify the best
subset of nodes to label in order to learn this relationship between node
attributes and network links. We can then use this discovered relationship to
accurately predict the class labels of the rest of the network nodes.
We present a model that identifies groups of nodes with similar link
patterns, which we call network roles, using a generative blockmodel. The model
then predicts labels by learning the mapping from network roles to class labels
using a maximum margin classifier. We choose a subset of nodes to label
according to an iterative margin-based active learning strategy. By integrating
the discovery of network roles with the classifier optimisation, the active
learning process can adapt the network roles to better represent the network
for node classification. We demonstrate the model by exploring a selection of
real world networks, including a marine food web and a network of English
words. We show that, in contrast to other network classifiers, this model
achieves good classification accuracy for a range of networks with different
relationships between class labels and network links
Unsupervised Domain Adaptation with Similarity Learning
The objective of unsupervised domain adaptation is to leverage features from
a labeled source domain and learn a classifier for an unlabeled target domain,
with a similar but different data distribution. Most deep learning approaches
to domain adaptation consist of two steps: (i) learn features that preserve a
low risk on labeled samples (source domain) and (ii) make the features from
both domains to be as indistinguishable as possible, so that a classifier
trained on the source can also be applied on the target domain. In general, the
classifiers in step (i) consist of fully-connected layers applied directly on
the indistinguishable features learned in (ii). In this paper, we propose a
different way to do the classification, using similarity learning. The proposed
method learns a pairwise similarity function in which classification can be
performed by computing similarity between prototype representations of each
category. The domain-invariant features and the categorical prototype
representations are learned jointly and in an end-to-end fashion. At inference
time, images from the target domain are compared to the prototypes and the
label associated with the one that best matches the image is outputed. The
approach is simple, scalable and effective. We show that our model achieves
state-of-the-art performance in different unsupervised domain adaptation
scenarios
Compositional Model based Fisher Vector Coding for Image Classification
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and
Machine Intelligence (TPAMI
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