67 citations · 309 across the 11 of their papers we have counts for
5 papers · 1 filter
Attribute Propagation Network for Graph Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +2
The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relat…
Interpretable Time-series Classification on Few-shot Samples
Wensi Tang, Lu Liu, Guodong Long
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series cla…
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
Lu Liu, Tianyi Zhou, Guodong Long +2
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problem…
A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William Hamilton, Guodong Long +2
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification,…
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Wensi Tang, Guodong Long, Lu Liu +3
The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large effo…