most citedKnow Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training

13 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20251 cited

Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals

Michaela Cohrs, Shiwoo Koak, Yejin Lee +4

Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible…

cs.CV20253 cited

Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models

Solha Kang, Joris Vankerschaver, Utku Ozbulak

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural netwo…

cs.CV20251 cited

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?

Utku Ozbulak, Esla Timothy Anzaku, Solha Kang +2

Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research eff…

q-bio.GN2023

BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants

Woowon Jang, Shiwoo Koak, Jiwon Im +2

BRCA genes, comprising BRCA1 and BRCA2 play indispensable roles in preserving genomic stability and facilitating DNA repair mechanisms. The presence of germline mutations in these…

cs.CV202313 cited

Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training

Utku Ozbulak, Hyun Jung Lee, Beril Boga +5

Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has di…