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Found 5 papers, 4 papers with code
MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis
Diffusion models have recently gained significant traction due to their ability to generate high-fidelity and diverse images and videos conditioned on text prompts.
Reproducible scaling laws for contrastive language-image learning
To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository.
Ranked #1 on
Zero-Shot Image Classification
on Country211
(using extra training data)
LAION-5B: An open large-scale dataset for training next generation image-text models
We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale.
ResNet strikes back: An improved training procedure in timm
We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work.
Ranked #2 on
Medical Image Classification
on NCT-CRC-HE-100K
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
