25,030 research outputs found
Interval Bound Propagation\unicode{x2013}aided Few\unicode{x002d}shot Learning
Few-shot learning aims to transfer the knowledge acquired from training on a
diverse set of tasks, from a given task distribution, to generalize to unseen
tasks, from the same distribution, with a limited amount of labeled data. The
underlying requirement for effective few-shot generalization is to learn a good
representation of the task manifold. One way to encourage this is to preserve
local neighborhoods in the feature space learned by the few-shot learner. To
this end, we introduce the notion of interval bounds from the provably robust
training literature to few-shot learning. The interval bounds are used to
characterize neighborhoods around the training tasks. These neighborhoods can
then be preserved by minimizing the distance between a task and its respective
bounds. We further introduce a novel strategy to artificially form new tasks
for training by interpolating between the available tasks and their respective
interval bounds, to aid in cases with a scarcity of tasks. We apply our
framework to both model-agnostic meta-learning as well as prototype-based
metric-learning paradigms. The efficacy of our proposed approach is evident
from the improved performance on several datasets from diverse domains in
comparison to a sizable number of recent competitors
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Some images that are difficult to recognize on their own may become more
clear in the context of a neighborhood of related images with similar
social-network metadata. We build on this intuition to improve multilabel image
annotation. Our model uses image metadata nonparametrically to generate
neighborhoods of related images using Jaccard similarities, then uses a deep
neural network to blend visual information from the image and its neighbors.
Prior work typically models image metadata parametrically, in contrast, our
nonparametric treatment allows our model to perform well even when the
vocabulary of metadata changes between training and testing. We perform
comprehensive experiments on the NUS-WIDE dataset, where we show that our model
outperforms state-of-the-art methods for multilabel image annotation even when
our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201
Parametric Local Metric Learning for Nearest Neighbor Classification
We study the problem of learning local metrics for nearest neighbor
classification. Most previous works on local metric learning learn a number of
local unrelated metrics. While this "independence" approach delivers an
increased flexibility its downside is the considerable risk of overfitting. We
present a new parametric local metric learning method in which we learn a
smooth metric matrix function over the data manifold. Using an approximation
error bound of the metric matrix function we learn local metrics as linear
combinations of basis metrics defined on anchor points over different regions
of the instance space. We constrain the metric matrix function by imposing on
the linear combinations manifold regularization which makes the learned metric
matrix function vary smoothly along the geodesics of the data manifold. Our
metric learning method has excellent performance both in terms of predictive
power and scalability. We experimented with several large-scale classification
problems, tens of thousands of instances, and compared it with several state of
the art metric learning methods, both global and local, as well as to SVM with
automatic kernel selection, all of which it outperforms in a significant
manner
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