1 citations · 1 across the 1 of their papers we have counts for
6 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…
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Andrea Pilzer, Stéphane Lathuilière, Dan Xu +3
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during…
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
Mihai Marian Puscas, Dan Xu, Andrea Pilzer +1
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Advers…
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning
Oleksiy Ostapenko, Mihai Puscas, Tassilo Klein +2
Models trained in the context of continual learning (CL) should be able to learn from a stream of data over an undefined period of time. The main challenges herein are: 1) maintain…
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…
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
Andrea Pilzer, Dan Xu, Mihai Marian Puscas +2
While recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance, costly ground truth annotations are required during tra…