8,713 research outputs found
Semantically Consistent Regularization for Zero-Shot Recognition
The role of semantics in zero-shot learning is considered. The effectiveness
of previous approaches is analyzed according to the form of supervision
provided. While some learn semantics independently, others only supervise the
semantic subspace explained by training classes. Thus, the former is able to
constrain the whole space but lacks the ability to model semantic correlations.
The latter addresses this issue but leaves part of the semantic space
unsupervised. This complementarity is exploited in a new convolutional neural
network (CNN) framework, which proposes the use of semantics as constraints for
recognition.Although a CNN trained for classification has no transfer ability,
this can be encouraged by learning an hidden semantic layer together with a
semantic code for classification. Two forms of semantic constraints are then
introduced. The first is a loss-based regularizer that introduces a
generalization constraint on each semantic predictor. The second is a codeword
regularizer that favors semantic-to-class mappings consistent with prior
semantic knowledge while allowing these to be learned from data. Significant
improvements over the state-of-the-art are achieved on several datasets.Comment: Accepted to CVPR 201
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Image to Image Translation for Domain Adaptation
We propose a general framework for unsupervised domain adaptation, which
allows deep neural networks trained on a source domain to be tested on a
different target domain without requiring any training annotations in the
target domain. This is achieved by adding extra networks and losses that help
regularize the features extracted by the backbone encoder network. To this end
we propose the novel use of the recently proposed unpaired image-toimage
translation framework to constrain the features extracted by the encoder
network. Specifically, we require that the features extracted are able to
reconstruct the images in both domains. In addition we require that the
distribution of features extracted from images in the two domains are
indistinguishable. Many recent works can be seen as specific cases of our
general framework. We apply our method for domain adaptation between MNIST,
USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in
classification tasks, and also between GTA5 and Cityscapes datasets for a
segmentation task. We demonstrate state of the art performance on each of these
datasets
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
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