5 research outputs found
Diversity Transfer Network for Few-Shot Learning
Few-shot learning is a challenging task that aims at training a classifier
for unseen classes with only a few training examples. The main difficulty of
few-shot learning lies in the lack of intra-class diversity within insufficient
training samples. To alleviate this problem, we propose a novel generative
framework, Diversity Transfer Network (DTN), that learns to transfer latent
diversities from known categories and composite them with support features to
generate diverse samples for novel categories in feature space. The learning
problem of the sample generation (i.e., diversity transfer) is solved via
minimizing an effective meta-classification loss in a single-stage network,
instead of the generative loss in previous works.
Besides, an organized auxiliary task co-training over known categories is
proposed to stabilize the meta-training process of DTN. We perform extensive
experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet,
CIFAR100 and CUB. The results show that DTN, with single-stage training and
faster convergence speed, obtains the state-of-the-art results among the
feature generation based few-shot learning methods. Code and supplementary
material are available at: \texttt{https://github.com/Yuxin-CV/DTN}Comment: 9 pages, 3 figures, AAAI 202
Learning from Very Few Samples: A Survey
Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches.Comment: 30 page
A Dual Attention Network with Semantic Embedding for Few-Shot Learning
Despite recent success of deep neural networks, it remains challenging to efficiently learn new visual concepts from limited training data. To address this problem, a prevailing strategy is to build a meta-learner that learns prior knowledge on learning from a small set of annotated data. However, most of existing meta-learning approaches rely on a global representation of images and a meta-learner with complex model structures, which are sensitive to background clutter and difficult to interpret. We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction. We implement our method via a dual-attention network and design a semantic-aware meta-learning loss to train the meta-learner network in an end-to-end manner. We validate our model on three few-shot image classification datasets with extensive ablative study, and our approach shows competitive performances over these datasets with fewer parameters. For facilitating the future research, code and data split are available: https://github.com/tonysy/STANet-PyTorc