Bridging the Gap Between Few-Shot and Many-Shot Learning via Distribution Cal…

A major gap between few-shot and many-shot learning is the data distribution empirically oserved by the model during training. In few-shot learning, the learned model can easily become over-fitted …

Transferable Coupled Network for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) aims at searching corresponding natural images with the given free-hand sketches, under the more realistic and challenging scenario of Zero-Shot Lea…

Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation

This paper studies instance-dependent P ositive and U nlabeled (PU) classification, where whether a positive example will be labeled (indicated by s ) is not only related to the class label y , but…

Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain…

Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on p…

Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically …

Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning

Multiple Kernel k-Means with Incomplete Kernels

Absent Multiple Kernel Learning Algorithms

Classification with Noisy Labels by Importance Reweighting