26 citations · 72 across the 18 of their papers we have counts for
3 papers · 2 filters
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Xuxi Chen, Yu Yang, Zhangyang Wang +1
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar genera…
Sieve: Multimodal Dataset Pruning Using Image Captioning Models
Anas Mahmoud, Mostafa Elhoushi, Amro Abbas +4
Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. This underscores the critical need for dataset pruning, as the quality of these data…
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Hritik Bansal, Nishad Singhi, Yu Yang +3
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have…