Transfer, low-shot, semi- and un-supervised learning

Association Graph Learning for Multi-Task Classification with Category Shifts

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the …

Variational Model Perturbation for Source-Free Domain Adaptation

We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains. The challenges stem from the distribution shift from the source to the target domain, coupled with the unavailability of …

Dynamic Prototype Convolution Network for Few-shot Semantic Segmentation

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among sup- port and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods im- plement such …

Hierarchical Variational Memory for Few-shot Learning Across Domains

Neural memory enables fast adaptation to new tasks with just a few training samples. Existing memory models store features only from the single last layer, which does not generalize well in presence of a domain shift between training and test …

Meta-learning for fast cross-lingual adaptation in dependency parsing

Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks. We apply model-agnostic meta-learning (MAML) to the task of cross-lingual …

Variational Multi-Task Learning with Gumbel-Softmax Priors

Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is available for each task. To tackle this challenge, we propose variational …

Learning to Adapt with Memory for Probabilistic Few-Shot Learning

Few-shot learning has recently generated increasing popularity in machine learning, which addresses the fundamental yet challenging problem of learning to adapt to new tasks with the limited data. In this paper, we propose a new probabilistic …

Arae: Adversarially robust training of autoencoders improves novelty detection

Autoencoders (AE) have recently been widely employed to approach the novelty detection problem. Trained only on the normal data, the AE is expected to reconstruct the normal data effectively while fail to regenerate the anomalous data, which could be …

Learning to Learn Dense Gaussian Processes for Few-Shot Learning

Gaussian processes with deep neural networks demonstrate to be a strong learner for few-shot learning since they combine the strength of deep learning and kernels while being able to well capture uncertainty. However, it remains an open problem to …

Variational prototype inference for few-shot semantic segmentation

In this paper, we propose variational prototype inference to address few-shot semantic segmentation in a probabilistic framework. A probabilistic latent variable model infers the distribution of the prototype that is treated as the latent variable. …