367 citations · 427 across the 8 of their papers we have counts for
12 papers · 1 filter
Multimodal Prototypical Networks for Few-shot Learning
Frederik Pahde, Mihai Puscas, Tassilo Klein +1
Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios. However, if data i…
Learning Graph-Based Priors for Generalized Zero-Shot Learning
Colin Samplawski, Jannik Wolff, Tassilo Klein +1
The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side informa…
Pruning at a Glance: Global Neural Pruning for Model Compression
Abdullah Salama, Oleksiy Ostapenko, Tassilo Klein +1
Deep Learning models have become the dominant approach in several areas due to their high performance. Unfortunately, the size and hence computational requirements of operating suc…
Multimodal Self-Supervised Learning for Medical Image Analysis
Aiham Taleb, Christoph Lippert, Tassilo Klein +1
Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learni…
Budget-Aware Adapters for Multi-Domain Learning
Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi +4
Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e…
Low-Shot Learning from Imaginary 3D Model
Frederik Pahde, Mihai Puscas, Jannik Wolff +3
Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-a…