161 research outputs found
Imagination Based Sample Construction for Zero-Shot Learning
Zero-shot learning (ZSL) which aims to recognize unseen classes with no
labeled training sample, efficiently tackles the problem of missing labeled
data in image retrieval. Nowadays there are mainly two types of popular methods
for ZSL to recognize images of unseen classes: probabilistic reasoning and
feature projection. Different from these existing types of methods, we propose
a new method: sample construction to deal with the problem of ZSL. Our proposed
method, called Imagination Based Sample Construction (IBSC), innovatively
constructs image samples of target classes in feature space by mimicking human
associative cognition process. Based on an association between attribute and
feature, target samples are constructed from different parts of various
samples. Furthermore, dissimilarity representation is employed to select
high-quality constructed samples which are used as labeled data to train a
specific classifier for those unseen classes. In this way, zero-shot learning
is turned into a supervised learning problem. As far as we know, it is the
first work to construct samples for ZSL thus, our work is viewed as a baseline
for future sample construction methods. Experiments on four benchmark datasets
show the superiority of our proposed method.Comment: Accepted as a short paper in ACM SIGIR 201
A Generative Model For Zero Shot Learning Using Conditional Variational Autoencoders
Zero shot learning in Image Classification refers to the setting where images
from some novel classes are absent in the training data but other information
such as natural language descriptions or attribute vectors of the classes are
available. This setting is important in the real world since one may not be
able to obtain images of all the possible classes at training. While previous
approaches have tried to model the relationship between the class attribute
space and the image space via some kind of a transfer function in order to
model the image space correspondingly to an unseen class, we take a different
approach and try to generate the samples from the given attributes, using a
conditional variational autoencoder, and use the generated samples for
classification of the unseen classes. By extensive testing on four benchmark
datasets, we show that our model outperforms the state of the art, particularly
in the more realistic generalized setting, where the training classes can also
appear at the test time along with the novel classes
Open-Category Classification by Adversarial Sample Generation
In real-world classification tasks, it is difficult to collect training
samples from all possible categories of the environment. Therefore, when an
instance of an unseen class appears in the prediction stage, a robust
classifier should be able to tell that it is from an unseen class, instead of
classifying it to be any known category. In this paper, adopting the idea of
adversarial learning, we propose the ASG framework for open-category
classification. ASG generates positive and negative samples of seen categories
in the unsupervised manner via an adversarial learning strategy. With the
generated samples, ASG then learns to tell seen from unseen in the supervised
manner. Experiments performed on several datasets show the effectiveness of
ASG.Comment: Published in IJCAI 201
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