Why You Should Retool Your AI Training Set (Not Your Model) скачать в хорошем качестве

Why You Should Retool Your AI Training Set (Not Your Model)

Large multimodal datasets have been instrumental in recent breakthroughs such as CLIP, Stable Diffusion, and GPT-4. At the same time, datasets rarely receive the same attention as model architectures or training algorithms. To address this shortcoming in the ML ecosystem, Ludwig Schmidt, Assistant Professor of Computer Science at the University of Washington, introduces DataComp, a benchmark where the training code is fixed and researchers innovate by proposing new training sets. Professor Schmidt and his colleagues provide a testbed for dataset experiments centered around a new candidate pool of 12.8B image-text pairs from Common Crawl. Participants in their benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running their standardized CLIP training code and testing on 38 downstream test sets. The DataComp benchmark consists of multiple scales, which facilitate the study of scaling trends and make the benchmark accessible to researchers with varying resources. Their baseline experiments show that the DataComp workflow is a promising way of improving multimodal datasets. Schmidt and his team introduce a new dataset DataComp-1B and show that CLIP models trained on this dataset outperform OpenAI’s CLIP model by 3.7 percentage points on ImageNet while using the same compute budget. Compared to LAION-5B, their data improvement corresponds to a 9x improvement in compute cost. More related videos:    • Generative AI Projects | Snorkel AI   More related videos:    • The Future of Data-Centric AI 2023   More related videos:    • Data-centric AI | Snorkel AI   More related videos:    • AI Data Development: Building Better AI Th...   #datacomp #multimodalai #machinelearningresearch