5 research outputs found
One-Shot Image Classification by Learning to Restore Prototypes
One-shot image classification aims to train image classifiers over the
dataset with only one image per category. It is challenging for modern deep
neural networks that typically require hundreds or thousands of images per
class. In this paper, we adopt metric learning for this problem, which has been
applied for few- and many-shot image classification by comparing the distance
between the test image and the center of each class in the feature space.
However, for one-shot learning, the existing metric learning approaches would
suffer poor performance because the single training image may not be
representative of the class. For example, if the image is far away from the
class center in the feature space, the metric-learning based algorithms are
unlikely to make correct predictions for the test images because the decision
boundary is shifted by this noisy image. To address this issue, we propose a
simple yet effective regression model, denoted by RestoreNet, which learns a
class agnostic transformation on the image feature to move the image closer to
the class center in the feature space. Experiments demonstrate that RestoreNet
obtains superior performance over the state-of-the-art methods on a broad range
of datasets. Moreover, RestoreNet can be easily combined with other methods to
achieve further improvement.Comment: Published as a conference paper in AAAI 202
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Few-shot learning aims to adapt models trained on the base dataset to novel
tasks where the categories are not seen by the model before. This often leads
to a relatively uniform distribution of feature values across channels on novel
classes, posing challenges in determining channel importance for novel tasks.
Standard few-shot learning methods employ geometric similarity metrics such as
cosine similarity and negative Euclidean distance to gauge the semantic
relatedness between two features. However, features with high geometric
similarities may carry distinct semantics, especially in the context of
few-shot learning. In this paper, we demonstrate that the importance ranking of
feature channels is a more reliable indicator for few-shot learning than
geometric similarity metrics. We observe that replacing the geometric
similarity metric with Kendall's rank correlation only during inference is able
to improve the performance of few-shot learning across a wide range of datasets
with different domains. Furthermore, we propose a carefully designed
differentiable loss for meta-training to address the non-differentiability
issue of Kendall's rank correlation. Extensive experiments demonstrate that the
proposed rank-correlation-based approach substantially enhances few-shot
learning performance
Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine
Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning from partially labeled data, which is useful for sequential learning in environments with scarcely labeled data. Unfortunately, the manifold regularization technique does not work out of the box as it requires determining the radial basis function (RBF) kernel width parameter. The RBF kernel width parameter directly impacts the performance as it is used to inform the model to which class each piece of data most likely belongs. The width parameter is often determined off-line via hyperparameter search, where a vast amount of labeled data is required. Therefore, it limits its utility in applications where it is difficult to collect a great deal of labeled data, such as data stream mining. To address this issue, we proposed eliminating the RBF kernel from the manifold regularization technique altogether by combining the manifold regularization technique with a prototype learning method, which uses a finite set of prototypes to approximate the entire data set. Compared to other manifold regularization approaches, this approach instead queries the prototype-based learner to find the most similar samples for each sample instead of relying on the RBF kernel. Thus, it no longer necessitates the RBF kernel, which improves its practicality. The proposed approach can learn faster and achieve a higher classification performance than other manifold regularization techniques based on experiments on benchmark data sets. Results showed that the proposed approach can perform well even without using the RBF kernel, which improves the practicality of manifold regularization techniques for semi-supervised learning