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Found 5 papers, 4 papers with code
Text Quality-Based Pruning for Efficient Training of Language Models
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious.
Demystifying CLIP Data
We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on MS COCO
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Ranked #1 on
Image Classification
on CIFAR-10
(using extra training data)
CiT: Curation in Training for Effective Vision-Language Data
Large vision-language models are generally applicable to many downstream tasks, but come at an exorbitant training cost that only large institutions can afford.
