3 papers
cs.LG2026
SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
Kacper Jurek, Wojciech Batko, Marek Åmieja +3
Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for applications involving tabular…
cs.LG2024
HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks
M. PrzewiÄźlikowski, P. Przybysz, J. Tabor +2
The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-S…
cs.CV2024
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
PaweÅ Batorski, Dawid Malarz, Marcin PrzewiÄźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…