19,774 research outputs found
Neural network-based clustering using pairwise constraints
This paper presents a neural network-based end-to-end clustering framework.
We design a novel strategy to utilize the contrastive criteria for pushing
data-forming clusters directly from raw data, in addition to learning a feature
embedding suitable for such clustering. The network is trained with weak
labels, specifically partial pairwise relationships between data instances. The
cluster assignments and their probabilities are then obtained at the output
layer by feed-forwarding the data. The framework has the interesting
characteristic that no cluster centers need to be explicitly specified, thus
the resulting cluster distribution is purely data-driven and no distance
metrics need to be predefined. The experiments show that the proposed approach
beats the conventional two-stage method (feature embedding with k-means) by a
significant margin. It also compares favorably to the performance of the
standard cross entropy loss for classification. Robustness analysis also shows
that the method is largely insensitive to the number of clusters. Specifically,
we show that the number of dominant clusters is close to the true number of
clusters even when a large k is used for clustering.Comment: ICLR 201
A probabilistic constrained clustering for transfer learning and image category discovery
Neural network-based clustering has recently gained popularity, and in
particular a constrained clustering formulation has been proposed to perform
transfer learning and image category discovery using deep learning. The core
idea is to formulate a clustering objective with pairwise constraints that can
be used to train a deep clustering network; therefore the cluster assignments
and their underlying feature representations are jointly optimized end-to-end.
In this work, we provide a novel clustering formulation to address scalability
issues of previous work in terms of optimizing deeper networks and larger
amounts of categories. The proposed objective directly minimizes the negative
log-likelihood of cluster assignment with respect to the pairwise constraints,
has no hyper-parameters, and demonstrates improved scalability and performance
on both supervised learning and unsupervised transfer learning.Comment: CVPR 2018 Deep-Vision Worksho
Triad-based Neural Network for Coreference Resolution
We propose a triad-based neural network system that generates affinity scores
between entity mentions for coreference resolution. The system simultaneously
accepts three mentions as input, taking mutual dependency and logical
constraints of all three mentions into account, and thus makes more accurate
predictions than the traditional pairwise approach. Depending on system
choices, the affinity scores can be further used in clustering or mention
ranking. Our experiments show that a standard hierarchical clustering using the
scores produces state-of-art results with gold mentions on the English portion
of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features
and is easy to train and use. The triads can also be easily extended to polyads
of higher orders. To our knowledge, this is the first neural network system to
model mutual dependency of more than two members at mention level
Learning to cluster in order to transfer across domains and tasks
This paper introduces a novel method to perform transfer learning across
domains and tasks, formulating it as a problem of learning to cluster. The key
insight is that, in addition to features, we can transfer similarity
information and this is sufficient to learn a similarity function and
clustering network to perform both domain adaptation and cross-task transfer
learning. We begin by reducing categorical information to pairwise constraints,
which only considers whether two instances belong to the same class or not.
This similarity is category-agnostic and can be learned from data in the source
domain using a similarity network. We then present two novel approaches for
performing transfer learning using this similarity function. First, for
unsupervised domain adaptation, we design a new loss function to regularize
classification with a constrained clustering loss, hence learning a clustering
network with the transferred similarity metric generating the training inputs.
Second, for cross-task learning (i.e., unsupervised clustering with unseen
categories), we propose a framework to reconstruct and estimate the number of
semantic clusters, again using the clustering network. Since the similarity
network is noisy, the key is to use a robust clustering algorithm, and we show
that our formulation is more robust than the alternative constrained and
unconstrained clustering approaches. Using this method, we first show state of
the art results for the challenging cross-task problem, applied on Omniglot and
ImageNet. Our results show that we can reconstruct semantic clusters with high
accuracy. We then evaluate the performance of cross-domain transfer using
images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both
datasets. Our approach doesn't explicitly deal with domain discrepancy. If we
combine with a domain adaptation loss, it shows further improvement.Comment: ICLR 201
Deep Transductive Semi-supervised Maximum Margin Clustering
Semi-supervised clustering is an very important topic in machine learning and
computer vision. The key challenge of this problem is how to learn a metric,
such that the instances sharing the same label are more likely close to each
other on the embedded space. However, little attention has been paid to learn
better representations when the data lie on non-linear manifold. Fortunately,
deep learning has led to great success on feature learning recently. Inspired
by the advances of deep learning, we propose a deep transductive
semi-supervised maximum margin clustering approach. More specifically, given
pairwise constraints, we exploit both labeled and unlabeled data to learn a
non-linear mapping under maximum margin framework for clustering analysis.
Thus, our model unifies transductive learning, feature learning and maximum
margin techniques in the semi-supervised clustering framework. We pretrain the
deep network structure with restricted Boltzmann machines (RBMs) layer by layer
greedily, and optimize our objective function with gradient descent. By
checking the most violated constraints, our approach updates the model
parameters through error backpropagation, in which deep features are learned
automatically. The experimental results shows that our model is significantly
better than the state of the art on semi-supervised clustering.Comment: 1
A Framework for Deep Constrained Clustering -- Algorithms and Advances
The area of constrained clustering has been extensively explored by
researchers and used by practitioners. Constrained clustering formulations
exist for popular algorithms such as k-means, mixture models, and spectral
clustering but have several limitations. A fundamental strength of deep
learning is its flexibility, and here we explore a deep learning framework for
constrained clustering and in particular explore how it can extend the field of
constrained clustering. We show that our framework can not only handle standard
together/apart constraints (without the well documented negative effects
reported earlier) generated from labeled side information but more complex
constraints generated from new types of side information such as continuous
values and high-level domain knowledge.Comment: Updated for ECML/PKDD 201
Face Clustering: Representation and Pairwise Constraints
Clustering face images according to their identity has two important
applications: (i) grouping a collection of face images when no external labels
are associated with images, and (ii) indexing for efficient large scale face
retrieval. The clustering problem is composed of two key parts: face
representation and choice of similarity for grouping faces. We first propose a
representation based on ResNet, which has been shown to perform very well in
image classification problems. Given this representation, we design a
clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly
estimates the adjacency matrix only based on the similarity between face
images. This allows a dynamic selection of number of clusters and retains
pairwise similarity between faces. ConPaC formulates the clustering problem as
a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to
find an approximate solution for maximizing the posterior probability of the
adjacency matrix. Experimental results on two benchmark face datasets (LFW and
IJB-B) show that ConPaC outperforms well known clustering algorithms such as
k-means, spectral clustering and approximate rank-order. Additionally, our
algorithm can naturally incorporate pairwise constraints to obtain a
semi-supervised version that leads to improved clustering performance. We also
propose an k-NN variant of ConPaC, which has a linear time complexity given a
k-NN graph, suitable for large datasets.Comment: This second version is the same as TIFS version. Some experiment
results are different from v1 because we correct the protocol
Semi-crowdsourced Clustering with Deep Generative Models
We consider the semi-supervised clustering problem where crowdsourcing
provides noisy information about the pairwise comparisons on a small subset of
data, i.e., whether a sample pair is in the same cluster. We propose a new
approach that includes a deep generative model (DGM) to characterize low-level
features of the data, and a statistical relational model for noisy pairwise
annotations on its subset. The two parts share the latent variables. To make
the model automatically trade-off between its complexity and fitting data, we
also develop its fully Bayesian variant. The challenge of inference is
addressed by fast (natural-gradient) stochastic variational inference
algorithms, where we effectively combine variational message passing for the
relational part and amortized learning of the DGM under a unified framework.
Empirical results on synthetic and real-world datasets show that our model
outperforms previous crowdsourced clustering methods.Comment: 32nd Conference on Neural Information Processing Systems (NIPS 2018
Human-like Clustering with Deep Convolutional Neural Networks
Classification and clustering have been studied separately in machine
learning and computer vision. Inspired by the recent success of deep learning
models in solving various vision problems (e.g., object recognition, semantic
segmentation) and the fact that humans serve as the gold standard in assessing
clustering algorithms, here, we advocate for a unified treatment of the two
problems and suggest that hierarchical frameworks that progressively build
complex patterns on top of the simpler ones (e.g., convolutional neural
networks) offer a promising solution. We do not dwell much on the learning
mechanisms in these frameworks as they are still a matter of debate, with
respect to biological constraints. Instead, we emphasize on the
compositionality of the real world structures and objects. In particular, we
show that CNNs, trained end to end using back propagation with noisy labels,
are able to cluster data points belonging to several overlapping shapes, and do
so much better than the state of the art algorithms. The main takeaway lesson
from our study is that mechanisms of human vision, particularly the hierarchal
organization of the visual ventral stream should be taken into account in
clustering algorithms (e.g., for learning representations in an unsupervised
manner or with minimum supervision) to reach human level clustering
performance. This, by no means, suggests that other methods do not hold merits.
For example, methods relying on pairwise affinities (e.g., spectral clustering)
have been very successful in many scenarios but still fail in some cases (e.g.,
overlapping clusters)
Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons
A great deal of work aims to discover large general purpose models of image
interest or memorability for visual search and information retrieval. This
paper argues that image interest is often domain and user specific, and that
efficient mechanisms for learning about this domain-specific image interest as
quickly as possible, while limiting the amount of data-labelling required, are
often more useful to end-users. This work uses pairwise image comparisons to
reduce the labelling burden on these users, and introduces an image interest
estimation approach that performs similarly to recent data hungry deep learning
approaches trained using pairwise ranking losses. Here, we use a Gaussian
process model to interpolate image interest inferred using a Bayesian ranking
approach over image features extracted using a pre-trained convolutional neural
network. Results show that fitting a Gaussian process in high-dimensional image
feature space is not only computationally feasible, but also effective across a
broad range of domains. The proposed probabilistic interest estimation approach
produces image interests paired with uncertainties that can be used to identify
images for which additional labelling is required and measure inference
convergence, allowing for sample efficient active model training. Importantly,
the probabilistic formulation allows for effective visual search and
information retrieval when limited labelling data is available
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