1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
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…
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…