21,946 research outputs found
Unsupervised feature learning with discriminative encoder
In recent years, deep discriminative models have achieved extraordinary
performance on supervised learning tasks, significantly outperforming their
generative counterparts. However, their success relies on the presence of a
large amount of labeled data. How can one use the same discriminative models
for learning useful features in the absence of labels? We address this question
in this paper, by jointly modeling the distribution of data and latent features
in a manner that explicitly assigns zero probability to unobserved data. Rather
than maximizing the marginal probability of observed data, we maximize the
joint probability of the data and the latent features using a two step EM-like
procedure. To prevent the model from overfitting to our initial selection of
latent features, we use adversarial regularization. Depending on the task, we
allow the latent features to be one-hot or real-valued vectors and define a
suitable prior on the features. For instance, one-hot features correspond to
class labels and are directly used for the unsupervised and semi-supervised
classification task, whereas real-valued feature vectors are fed as input to
simple classifiers for auxiliary supervised discrimination tasks. The proposed
model, which we dub discriminative encoder (or DisCoder), is flexible in the
type of latent features that it can capture. The proposed model achieves
state-of-the-art performance on several challenging tasks.Comment: 10 pages, 4 figures, International Conference on Data Mining, 201
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
The main obstacle to weakly supervised semantic image segmentation is the
difficulty of obtaining pixel-level information from coarse image-level
annotations. Most methods based on image-level annotations use localization
maps obtained from the classifier, but these only focus on the small
discriminative parts of objects and do not capture precise boundaries.
FickleNet explores diverse combinations of locations on feature maps created by
generic deep neural networks. It selects hidden units randomly and then uses
them to obtain activation scores for image classification. FickleNet implicitly
learns the coherence of each location in the feature maps, resulting in a
localization map which identifies both discriminative and other parts of
objects. The ensemble effects are obtained from a single network by selecting
random hidden unit pairs, which means that a variety of localization maps are
generated from a single image. Our approach does not require any additional
training steps and only adds a simple layer to a standard convolutional neural
network; nevertheless it outperforms recent comparable techniques on the Pascal
VOC 2012 benchmark in both weakly and semi-supervised settings.Comment: To appear in CVPR 201
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