Scaling Language-Image Pre-training via Maskingarxiv.org/abs/2212.00794 从去年以来,以CLIP为代表的通过语言监督的视觉预训练(Language-supervised Visual Pre-training/ LIP)通过image/text pair的方式打破了传统的视觉预训练依靠超大规模标注数据集的瓶颈。LIP需要依靠超大规模的训练,以文章中的说法,通常在10000...
加速。同样的时间内,训练更多的image-text pairs 更大batch。因为CLIP对比学习的loss是batch越大负样本越多,因此目标函数中能组成更多的负样本,预期会带来较大的gain。 博主认为FLIP应该会成为vision-language learning的一个通用trick,至少在工业界会被快速广泛尝试和推广。原因很简单,FLIP是CLIP训练速度的3.7倍,基于...
In recent years, we have witnessed significant performance boost in the image captioning task based on vision-language pre-training (VLP). Scale is believed to be an important factor for this advance. However, most existing work only focuses on pre-training transformers with m...
Unified Vision-Language Pre-Training for Image Captioning and VQA.pdf You Need Multiple Exiting Dynamic Early Exiting for.pdf Cream Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models.pdf VLMO Unified Vision-Language Pre-Training with.pdf DocFo...
但预训练并不是提高模型涌现能力的唯一途径，加上在训练量带来的模型能力中，Pre-training Loss更多作为评估的早期指标。而且Loss值能随着算力、数据规模、参数量的提升呈现明显的线性下降，它背后的Scaling Law，很快成了模型智能涌现和性能提升的另一大关注点。Scaling law对模型性能的影响 Scaling Law，顾名思义，就...
但预训练并不是提高模型涌现能力的唯一途径,加上在训练量带来的模型能力中,Pre-training Loss更多作为评估的早期指标。 而且Loss值能随着算力、数据规模、参数量的提升呈现明显的线性下降,它背后的Scaling Law,很快成了模型智能涌现和性能提升的另一大关注点。
scaling research suggests that larger models need more datasets to train efficiently. According to the blog, the team created WebLI—a multilingual language-image dataset made from images and text readily available on the public web—in order to unlock the potential of language-image pretraining. ...
但预训练并不是提高模型涌现能力的唯一途径,加上在训练量带来的模型能力中,Pre-training Loss更多作为评估的早期指标。 而且Loss值能随着算力、数据规模、参数量的提升呈现明显的线性下降,它背后的Scaling Law,很快成了模型智能涌现和性能提升的另一大关注点。
Claude团队三巨头同时接受采访,回应一切。 整整5个小时,创始人Dario Amodei、Claude性格设计师Amanda Askell、机制可解释性先驱Chris Olah无所不谈,透露了关于模型、公司和行业的很多内幕和细节。 比如Claude 3.5 Opus仍有可能发布,公司今年从300人扩展到1000人, ...
We are able to scale various language, speech and vision models using the Mixture Of Experts technique by incorporating ORT MoE. We will continue to optimize the ORT MoE implementation to improve training throughput and explore new distribution strategies. This will enable d...