206 citations · 970 across the 33 of their papers we have counts for
34 papers
Inhibitor Transformers and Gated RNNs for Torus Efficient Fully Homomorphic Encryption
Rickard Brännvall, Tony Zhang, Henrik Forsgren +3
This paper introduces efficient modifications to neural network-based sequence processing approaches, laying new grounds for scalable privacy-preserving machine learning under Full…
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts
Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki
The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. T…
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa +4
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation…
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons
Mattias Nilsson, Ton Juny Pina, Lyes Khacef +3
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive…
Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions
Mattias Nilsson, Olov Schelén, Anders Lindgren +4
Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the r…
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics
Oskar Sjögren, Gustav Grund Pihlgren, Fredrik Sandin +1
Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been…