510 citations · 998 across the 19 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…
Self Paced Adversarial Training for Multimodal Few-shot Learning
Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen +2
State-of-the-art deep learning algorithms yield remarkable results in many visual recognition tasks. However, they still fail to provide satisfactory results in scarce data regimes…
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
Shailza Jolly, Sandro Pezzelle, Tassilo Klein +2
We introduce MASSES, a simple evaluation metric for the task of Visual Question Answering (VQA). In its standard form, the VQA task is operationalized as follows: Given an image an…
Cross-modal Hallucination for Few-shot Fine-grained Recognition
Frederik Pahde, Patrick Jähnichen, Tassilo Klein +1
State-of-the-art deep learning algorithms generally require large amounts of data for model training. Lack thereof can severely deteriorate the performance, particularly in scenari…
Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality
Benjamin Gutierrez Becker, Tassilo Klein, Christian Wachinger
Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohor…
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data
Benjamín Gutiérrez, Loïc Peter, Tassilo Klein +1
With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply inc…
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learn…