10 papers
Learning from Random Subspace Exploration: Generalized Test-Time Augmentation with Self-supervised Distillation
Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu
We introduce Generalized Test-Time Augmentation (GTTA), a highly effective method for improving the performance of a trained model, which unlike other existing Test-Time Augmentati…
Leveraging Lightweight Generators for Memory Efficient Continual Learning
Christiaan Lamers, Ahmed Nabil Belbachir, Thomas Bäck +1
Catastrophic forgetting can be trivially alleviated by keeping all data from previous tasks in memory. Therefore, minimizing the memory footprint while maximizing the amount of rel…
Learning Graph Representation of Agent Diffusers
Youcef Djenouri, Nassim Belmecheri, Tomasz Michalak +3
Diffusion-based generative models have significantly advanced text-to-image synthesis, demonstrating impressive text comprehension and zero-shot generalization. These models refine…
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu
Solving fish segmentation in underwater videos, a real-world problem of great practical value in marine and aquaculture industry, is a challenging task due to the difficulty of the…
Clustering-based Domain-Incremental Learning
Christiaan Lamers, Rene Vidal, Nabil Belbachir +3
We consider the problem of learning multiple tasks in a continual learning setting in which data from different tasks is presented to the learner in a streaming fashion. A key chal…
Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data
Muhammad Sulaiman, Mina Farmanbar, Ahmed Nabil Belbachir +1
Building segmentation is essential in infrastructure development, population management, and geological observations. This article targets shallow models due to their interpretable…
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Alina Marcu, Mihai Pirvu, Dragos Costea +5
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task…
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
Veysel Kocaman, Ofer M. Shir, Thomas Bäck +1
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast…
The Vision of Self-Evolving Computing Systems
Danny Weyns, Thomas Baeck, Rene Vidal +2
Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the…
Lifelong Computing
Danny Weyns, Thomas Bäck, Renè Vidal +2
Computing systems form the backbone of many aspects of our life, hence they are becoming as vital as water, electricity, and road infrastructures for our society. Yet, engineering…