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

Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery

In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined category set. While strides have been made in the realms of …

Order-preserving Consistency Regularization for Domain Adaptation and Generalization

Deep learning models fail on cross-domain challenges if the model is oversensitive to domain-specific attributes, e.g., lightning, background, camera angle, etc. To alleviate this problem, data augmentation coupled with consistency regularization are …

Dynamic Transformer for Few-shot Instance Segmentation

Few-shot instance segmentation aims to train an instance segmentation model that can fast adapt to novel classes with only a few reference images. Existing methods are usually derived from standard detection models and tackle few-shot instance …

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 …