3 papers
cs.CV2024★ 1 cited
Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters
Weizhi Wang, Khalil Mrini, Linjie Yang +4
We propose a novel framework for filtering image-text data by leveraging fine-tuned Multimodal Language Models (MLMs). Our approach outperforms predominant filtering methods (e.g.,…
cs.CV2023★ 2 cited
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering
Haichao Yu, Yu Tian, Sateesh Kumar +2
The quality of pre-training data plays a critical role in the performance of foundation models. Popular foundation models often design their own recipe for data filtering, which ma…
cs.SE2023
ConFL: Constraint-guided Fuzzing for Machine Learning Framework
Zhao Liu, Quanchen Zou, Tian Yu +4
As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) f…