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Nikolas Adaloglou
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A comprehensive study on deep learning-based methods for sign language recognition
N Adaloglou, T Chatzis, I Papastratis, A Stergioulas, GT Papadopoulos, ...
IEEE transactions on multimedia 24, 1750-1762, 2021
1642021
Intuitive Explanation of Skip Connections in Deep Learning
N Adaloglou
https://theaisummer.com/skip-connections/, 2020
412020
Exploring the Limits of Deep Image Clustering using Pretrained Models
N Adaloglou, F Michels, H Kalisch, M Kollmann
BMVC 2023, 2023
132023
Deep learning in medical image analysis: a comparative analysis of multi-modal brain-MRI segmentation with 3D deep neural networks
A Nikolaos
University of Patras, 2019
122019
Self-supervision advances morphological profiling by unlocking powerful image representations
V Kim, N Adaloglou, M Osterland, FM Morelli, M Halawa, T König, D Gnutt, ...
BioRxiv, 2023.04. 28.538691, 2023
11*2023
Understanding the receptive field of deep convolutional networks
N Adaloglou
https://theaisummer.com/receptive-field/, 2020
112020
Self-supervised anomaly detection by self-distillation and negative sampling
N Rafiee, R Gholamipoor, N Adaloglou, S Jaxy, J Ramakers, M Kollmann
International Conference on Artificial Neural Networks, 459-470, 2022
82022
Multi-view adaptive graph convolutions for graph classification
N Adaloglou, N Vretos, P Daras
ECCV 2020, 2020
82020
How attention works in deep learning: understanding the attention mechanism in sequence models
N Adaloglou, S Karagiannakos
https://theaisummer.com/attention/, 2019
72019
Best deep cnn architectures and their principles: From alexnet to efficientnet
N Adaloglou
AI Summer, 2021
6*2021
Transformers in Computer Vision
N Adaloglou
https://github.com/The-AI-Summer/self-attention-cv, 2021
62021
Diffusion models: toward state-of-the-art image generation
S Karagiannakos, N Adaloglou
https://theaisummer.com/diffusion-models/, 2022
42022
Why Multi-head Self Attention Works: Math, Intuitions and 10+ 1 Hidden Insights
N Adaloglou
https://theaisummer.com/self-attention/, 2021
42021
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
N Adaloglou, F Michels, T Kaiser, M Kollmann
Transactions on Machine Learning Research, 2835-8856, 2024
3*2024
How the Vision Transformer (ViT) works in 10 minutes: an image is worth 16x16 words
N Adaloglou
https://theaisummer.com/vision-transformer/, 2021
32021
Rethinking cluster-conditioned diffusion models
N Adaloglou, T Kaiser, F Michels, M Kollmann
arXiv preprint arXiv:2403.00570, 2024
22024
Scaling Up Deep Clustering Methods Beyond ImageNet-1K
N Adaloglou, F Michels, K Senft, D Petrusheva, M Kollmann
arXiv preprint arXiv:2406.01203, 2024
2024
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Articles 1–17