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7 papers · 1 filter
Transfer learning-based method for automated ewaste recycling in smart cities
Nermeen Abou Baker, Paul Szabo-Müller, Uwe Handmann
Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods…
Battery detection of XRay images using transfer learning
Nermeen Abou Baker, David Rohrschneider, Uwe Handmann
The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in predicting whether the image c…
Don't waste SAM
Nermeen Abou Baker, Uwe Handmann
Meta AI has recently released the Segment Anything Model (SAM), which demonstrates exceptional zero-shot image segmentation performance across various tasks with remarkable accurac…
Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks
Nermeen Abou Baker, David Rohrschneider, Uwe Handmann
Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art results across numerous tasks. H…
A Transfer Learning Evaluation of Deep Neural Networks for Image Classification
Nermeen Abou Baker, Nico Zengeler, Uwe Handmann
Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. Th…
Confidence Calibration for Object Detection and Segmentation
Fabian Küppers, Anselm Haselhoff, Jan Kronenberger +1
Calibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. Howe…