9,892 research outputs found
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
Deep metric learning has been widely applied in many computer vision tasks,
and recently, it is more attractive in \emph{zero-shot image retrieval and
clustering}(ZSRC) where a good embedding is requested such that the unseen
classes can be distinguished well. Most existing works deem this 'good'
embedding just to be the discriminative one and thus race to devise powerful
metric objectives or hard-sample mining strategies for leaning discriminative
embedding. However, in this paper, we first emphasize that the generalization
ability is a core ingredient of this 'good' embedding as well and largely
affects the metric performance in zero-shot settings as a matter of fact. Then,
we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to
explicitly optimize a robust metric. It is mainly achieved by introducing an
interesting Energy Confusion regularization term, which daringly breaks away
from the traditional metric learning idea of discriminative objective devising,
and seeks to 'confuse' the learned model so as to encourage its generalization
ability by reducing overfitting on the seen classes. We train this confusion
term together with the conventional metric objective in an adversarial manner.
Although it seems weird to 'confuse' the network, we show that our ECAML indeed
serves as an efficient regularization technique for metric learning and is
applicable to various conventional metric methods. This paper empirically and
experimentally demonstrates the importance of learning embedding with good
generalization, achieving state-of-the-art performances on the popular CUB,
CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks.
\textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Abstractive community detection is an important spoken language understanding
task, whose goal is to group utterances in a conversation according to whether
they can be jointly summarized by a common abstractive sentence. This paper
provides a novel approach to this task. We first introduce a neural contextual
utterance encoder featuring three types of self-attention mechanisms. We then
train it using the siamese and triplet energy-based meta-architectures.
Experiments on the AMI corpus show that our system outperforms multiple
energy-based and non-energy based baselines from the state-of-the-art. Code and
data are publicly available.Comment: Update baseline
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