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Found 5 papers, 4 papers with code
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.
Unsupervised Domain Adaptation for Video Transformers in Action Recognition
On the other hand, the performance of a model in action recognition is heavily affected by domain shift.
Self-Supervised Models are Continual Learners
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale.
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
Online Local Boosting: improving performance in online decision trees
To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees.
