2 citations · 5 across the 5 of their papers we have counts for
5 papers
JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data
Apurva Kalia, Yan Zhou Chen, Dilip Krishnan +1
Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra…
Learning Vision from Models Rivals Learning Vision from Data
Yonglong Tian, Lijie Fan, Kaifeng Chen +3
We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dat…
Scaling Laws of Synthetic Images for Model Training ... for Now
Lijie Fan, Kaifeng Chen, Dilip Krishnan +3
Recent significant advances in text-to-image models unlock the possibility of training vision systems using synthetic images, potentially overcoming the difficulty of collecting cu…
Improve Supervised Representation Learning with Masked Image Modeling
Kaifeng Chen, Daniel Salz, Huiwen Chang +3
Training visual embeddings with labeled data supervision has been the de facto setup for representation learning in computer vision. Inspired by recent success of adopting masked i…
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li, Sangnie Bhardwaj, Yonglong Tian +6
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalization capabilities. However, automatically…