3 research outputs found
Meta-Learning across Meta-Tasks for Few-Shot Learning
Existing meta-learning based few-shot learning (FSL) methods typically adopt
an episodic training strategy whereby each episode contains a meta-task. Across
episodes, these tasks are sampled randomly and their relationships are ignored.
In this paper, we argue that the inter-meta-task relationships should be
exploited and those tasks are sampled strategically to assist in meta-learning.
Specifically, we consider the relationships defined over two types of meta-task
pairs and propose different strategies to exploit them. (1) Two meta-tasks with
disjoint sets of classes: this pair is interesting because it is reminiscent of
the relationship between the source seen classes and target unseen classes,
featured with domain gap caused by class differences. A novel learning
objective termed meta-domain adaptation (MDA) is proposed to make the
meta-learned model more robust to the domain gap. (2) Two meta-tasks with
identical sets of classes: this pair is useful because it can be employed to
learn models that are robust against poorly sampled few-shots. To that end, a
novel meta-knowledge distillation (MKD) objective is formulated. There are some
mistakes in the experiments. We thus choose to withdraw this paper.Comment: There are some mistakes in the experiments. We thus choose to
withdraw this pape
High-order structure preserving graph neural network for few-shot learning
Few-shot learning can find the latent structure information between the prior
knowledge and the queried data by the similarity metric of meta-learning to
construct the discriminative model for recognizing the new categories with the
rare labeled samples. Most existing methods try to model the similarity
relationship of the samples in the intra tasks, and generalize the model to
identify the new categories. However, the relationship of samples between the
separated tasks is difficultly considered because of the different metric
criterion in the respective tasks. In contrast, the proposed high-order
structure preserving graph neural network(HOSP-GNN) can further explore the
rich structure of the samples to predict the label of the queried data on graph
that enables the structure evolution to explicitly discriminate the categories
by iteratively updating the high-order structure relationship (the relative
metric in multi-samples,instead of pairwise sample metric) with the manifold
structure constraints. HOSP-GNN can not only mine the high-order structure for
complementing the relevance between samples that may be divided into the
different task in meta-learning, and but also generate the rule of the
structure updating by manifold constraint. Furthermore, HOSP-GNN doesn't need
retrain the learning model for recognizing the new classes, and HOSP-GNN has
the well-generalizable high-order structure for model adaptability. Experiments
show that HOSP-GNN outperforms the state-of-the-art methods on supervised and
semi-supervised few-shot learning in three benchmark datasets that are
miniImageNet, tieredImageNet and FC100
Few-shot Learning with LSSVM Base Learner and Transductive Modules
The performance of meta-learning approaches for few-shot learning generally
depends on three aspects: features suitable for comparison, the classifier (
base learner ) suitable for low-data scenarios, and valuable information from
the samples to classify. In this work, we make improvements for the last two
aspects: 1) although there are many effective base learners, there is a
trade-off between generalization performance and computational overhead, so we
introduce multi-class least squares support vector machine as our base learner
which obtains better generation than existing ones with less computational
overhead; 2) further, in order to utilize the information from the query
samples, we propose two simple and effective transductive modules which modify
the support set using the query samples, i.e., adjusting the support samples
basing on the attention mechanism and adding the prototypes of the query set
with pseudo labels to the support set as the pseudo support samples. These two
modules significantly improve the few-shot classification accuracy, especially
for the difficult 1-shot setting. Our model, denoted as FSLSTM (Few-Shot
learning with LSsvm base learner and Transductive Modules), achieves
state-of-the-art performance on miniImageNet and CIFAR-FS few-shot learning
benchmarks.Comment: 9 pages,3 figures,3 table