1,770 research outputs found
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
Compressing Word Embeddings
Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.Comment: 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental
results were submitted to ICLR-2016, but the paper has been significantly
updated, since a new experimental set-up worked much bette
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize
novel classes with few examples? Pre-training based methods effectively tackle
the problem by pre-training a feature extractor and then predicting novel
classes via a cosine nearest neighbor classifier with mean-based prototypes.
Nevertheless, due to the data scarcity, the mean-based prototypes are usually
biased. In this paper, we attempt to diminish the prototype bias by regarding
it as a prototype optimization problem. To this end, we propose a novel
meta-learning based prototype optimization framework to rectify prototypes,
\emph{i.e.}, introducing a meta-optimizer to optimize prototypes. Although the
existing meta-optimizers can also be adapted to our framework, they all
overlook a crucial gradient bias issue, \emph{i.e.}, the mean-based gradient
estimation is also biased on sparse data. To address the issue, we regard the
gradient and its flow as meta-knowledge and then propose a novel Neural
Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes,
called MetaNODE. In this meta-optimizer, we first view the mean-based
prototypes as initial prototypes, and then model the process of prototype
optimization as continuous-time dynamics specified by a Neural ODE. A gradient
flow inference network is carefully designed to learn to estimate the
continuous gradient flow for prototype dynamics. Finally, the optimal
prototypes can be obtained by solving the Neural ODE. Extensive experiments on
miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our
method.Comment: Accepted by AAAI 202
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