3,502 research outputs found
CSI-Based Cross-Domain Activity Recognition via Zero-Shot Prototypical Networks
The cross-domain capability of wireless sensing is currently one of the major
challenges on human activity recognition (HAR) based on the channel state
information (CSI) of wireless signals. The difficulty of labeling samples from
new domains has encouraged the use of few and zero shot strategies. In this
context, prototype networks have attracted attention due to their reasonable
cross-domain transferability. This paper presents a novel zero-shot prototype
recurrent convolutional network that implements a zero-shot learning strategy
for HAR via CSI. This method extracts the prototypes from an available source
domain to classify unseen and unlabeled data from the target domain for the
same or similar classes. The experiments have been developed using three
datasets with real measurements, and the results include an inter-datasets
evaluation. Overall, the results improve the state of the art and make it a
promising solution for cross-domain HAR
Semantic Autoencoder for Zero-Shot Learning
Existing zero-shot learning (ZSL) models typically learn a projection
function from a feature space to a semantic embedding space (e.g.~attribute
space). However, such a projection function is only concerned with predicting
the training seen class semantic representation (e.g.~attribute prediction) or
classification. When applied to test data, which in the context of ZSL contains
different (unseen) classes without training data, a ZSL model typically suffers
from the project domain shift problem. In this work, we present a novel
solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
encoder-decoder paradigm, an encoder aims to project a visual feature vector
into the semantic space as in the existing ZSL models. However, the decoder
exerts an additional constraint, that is, the projection/code must be able to
reconstruct the original visual feature. We show that with this additional
reconstruction constraint, the learned projection function from the seen
classes is able to generalise better to the new unseen classes. Importantly,
the encoder and decoder are linear and symmetric which enable us to develop an
extremely efficient learning algorithm. Extensive experiments on six benchmark
datasets demonstrate that the proposed SAE outperforms significantly the
existing ZSL models with the additional benefit of lower computational cost.
Furthermore, when the SAE is applied to supervised clustering problem, it also
beats the state-of-the-art.Comment: accepted to CVPR201
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Deep neural networks are widely used for classification. These deep models
often suffer from a lack of interpretability -- they are particularly difficult
to understand because of their non-linear nature. As a result, neural networks
are often treated as "black box" models, and in the past, have been trained
purely to optimize the accuracy of predictions. In this work, we create a novel
network architecture for deep learning that naturally explains its own
reasoning for each prediction. This architecture contains an autoencoder and a
special prototype layer, where each unit of that layer stores a weight vector
that resembles an encoded training input. The encoder of the autoencoder allows
us to do comparisons within the latent space, while the decoder allows us to
visualize the learned prototypes. The training objective has four terms: an
accuracy term, a term that encourages every prototype to be similar to at least
one encoded input, a term that encourages every encoded input to be close to at
least one prototype, and a term that encourages faithful reconstruction by the
autoencoder. The distances computed in the prototype layer are used as part of
the classification process. Since the prototypes are learned during training,
the learned network naturally comes with explanations for each prediction, and
the explanations are loyal to what the network actually computes.Comment: The first two authors contributed equally, 8 pages, accepted in AAAI
201
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
- …