Self-regulation can curb students' overconfidence in AI

The rapid emergence of generative AI in higher education has raised concerns about students' reliance on the use of these ...
eLife

CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy

This important work presents a self-supervised method for the segmentation of 3D cells in fluorescent microscopy images, conveniently packaged as a Napari plugin and tested on an annotated dataset.
IEEE

Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples

Abstract: Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. The existing methods assume that the class ...
Frontiers

Contrastive self-supervised learning for neurodegenerative disorder classification

Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans.
GitHub

Self-Supervised Vision Transformers with DINO

PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supervised Vision Transformers. Run DINO with ViT-small network on a single node with 8 GPUs for 100 ...
Frontiers

Transfer learning for self-supervised, blind-spot seismic denoising

Noise is ever present in seismic data and arises from numerous sources and is continually evolving, both spatially and temporally. The use of supervised deep learning procedures for denoising of ...
IEEE

Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

Abstract: Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one ...