15,750 research outputs found
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
Low-shot learning with large-scale diffusion
This paper considers the problem of inferring image labels from images when
only a few annotated examples are available at training time. This setup is
often referred to as low-shot learning, where a standard approach is to
re-train the last few layers of a convolutional neural network learned on
separate classes for which training examples are abundant. We consider a
semi-supervised setting based on a large collection of images to support label
propagation. This is possible by leveraging the recent advances on large-scale
similarity graph construction.
We show that despite its conceptual simplicity, scaling label propagation up
to hundred millions of images leads to state of the art accuracy in the
low-shot learning regime
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Many interesting problems in machine learning are being revisited with new
deep learning tools. For graph-based semisupervised learning, a recent
important development is graph convolutional networks (GCNs), which nicely
integrate local vertex features and graph topology in the convolutional layers.
Although the GCN model compares favorably with other state-of-the-art methods,
its mechanisms are not clear and it still requires a considerable amount of
labeled data for validation and model selection. In this paper, we develop
deeper insights into the GCN model and address its fundamental limits. First,
we show that the graph convolution of the GCN model is actually a special form
of Laplacian smoothing, which is the key reason why GCNs work, but it also
brings potential concerns of over-smoothing with many convolutional layers.
Second, to overcome the limits of the GCN model with shallow architectures, we
propose both co-training and self-training approaches to train GCNs. Our
approaches significantly improve GCNs in learning with very few labels, and
exempt them from requiring additional labels for validation. Extensive
experiments on benchmarks have verified our theory and proposals.Comment: AAAI-2018 Oral Presentatio
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