3,824 research outputs found
Spiking neurons with short-term synaptic plasticity form superior generative networks
Spiking networks that perform probabilistic inference have been proposed both
as models of cortical computation and as candidates for solving problems in
machine learning. However, the evidence for spike-based computation being in
any way superior to non-spiking alternatives remains scarce. We propose that
short-term plasticity can provide spiking networks with distinct computational
advantages compared to their classical counterparts. In this work, we use
networks of leaky integrate-and-fire neurons that are trained to perform both
discriminative and generative tasks in their forward and backward information
processing paths, respectively. During training, the energy landscape
associated with their dynamics becomes highly diverse, with deep attractor
basins separated by high barriers. Classical algorithms solve this problem by
employing various tempering techniques, which are both computationally
demanding and require global state updates. We demonstrate how similar results
can be achieved in spiking networks endowed with local short-term synaptic
plasticity. Additionally, we discuss how these networks can even outperform
tempering-based approaches when the training data is imbalanced. We thereby
show how biologically inspired, local, spike-triggered synaptic dynamics based
simply on a limited pool of synaptic resources can allow spiking networks to
outperform their non-spiking relatives.Comment: corrected typo in abstrac
Generating Visual Representations for Zero-Shot Classification
This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on learning a common embedding space allowing to compare visual
features of unknown categories with semantic descriptions. This paper argues
that these approaches are limited as i) efficient discrimi-native classifiers
can't be used ii) classification tasks with seen and unseen categories
(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
In contrast , this paper suggests to address ZSC and GZSC by i) learning a
conditional generator using seen classes ii) generate artificial training
examples for the categories without exemplars. ZSC is then turned into a
standard supervised learning problem. Experiments with 4 generative models and
5 datasets experimentally validate the approach, giving state-of-the-art
results on both ZSC and GZSC
Large-Scale Visual Relationship Understanding
Large scale visual understanding is challenging, as it requires a model to
handle the widely-spread and imbalanced distribution of <subject, relation,
object> triples. In real-world scenarios with large numbers of objects and
relations, some are seen very commonly while others are barely seen. We develop
a new relationship detection model that embeds objects and relations into two
vector spaces where both discriminative capability and semantic affinity are
preserved. We learn both a visual and a semantic module that map features from
the two modalities into a shared space, where matched pairs of features have to
discriminate against those unmatched, but also maintain close distances to
semantically similar ones. Benefiting from that, our model can achieve superior
performance even when the visual entity categories scale up to more than
80,000, with extremely skewed class distribution. We demonstrate the efficacy
of our model on a large and imbalanced benchmark based of Visual Genome that
comprises 53,000+ objects and 29,000+ relations, a scale at which no previous
work has ever been evaluated at. We show superiority of our model over
carefully designed baselines on the original Visual Genome dataset with 80,000+
categories. We also show state-of-the-art performance on the VRD dataset and
the scene graph dataset which is a subset of Visual Genome with 200 categories
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