Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and decoding on different CPUs or GPUs should be error-free and with negligible performance reduction. In this paper, we present a method to solve the device interoperability problem of a state-of-the-art image compression network. We implement quantization to entropy networks which output entropy parameters. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly with minor performance deviation, of 0.3% BD-rate, from floating point model results.
translated by 谷歌翻译
Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is a key open challenge in creating reliable NLG systems. In this work, we introduce a framework to identify and remove low-quality training instances that lead to undesirable outputs, such as faithfulness errors in text summarization. We show that existing approaches for error tracing, such as gradient-based influence measures, do not perform reliably for detecting faithfulness errors in summarization. We overcome the drawbacks of existing error tracing methods through a new, contrast-based estimate that compares undesired generations to human-corrected outputs. Our proposed method can achieve a mean average precision of 0.91 across synthetic tasks with known ground truth and can achieve a two-fold reduction in hallucinations on a real entity hallucination evaluation on the NYT dataset.
translated by 谷歌翻译
Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.
translated by 谷歌翻译
Machine learning models are now able to convert user-written text descriptions into naturalistic images. These models are available to anyone online and are being used to generate millions of images a day. We investigate these models and find that they amplify dangerous and complex stereotypes. Moreover, we find that the amplified stereotypes are difficult to predict and not easily mitigated by users or model owners. The extent to which these image-generation models perpetuate and amplify stereotypes and their mass deployment is cause for serious concern.
translated by 谷歌翻译
表现良好的深度学习模型通常具有很高的计算成本。在本文中，我们结合了两种试图降低计算成本的方法，同时保持模型性能很高：修剪和提早出口网络。我们评估了修剪早期出口网络的两种方法：（1）立即修剪整个网络，（2）以有序的方式修剪基本网络和其他线性分类器。实验结果表明，一般而言，立即修剪整个网络是更好的策略。但是，以高精度的速度，这两种方法具有相似的性能，这意味着可以将修剪和提早出口的过程分开而不会丧失最佳性。
translated by 谷歌翻译
通常通过过去的选择来告知机器学习中的评估，例如要使用哪些数据集或指标。该标准化可以使用排行榜对平等基础进行比较，但是随着出现更好的替代方案，评估选择变得不佳。这个问题在自然语言生成中尤其相关，该语言需要不断改善的数据集，指标和人类评估以提出确定性的主张。为了使遵循最佳模型评估实践更加容易，我们介绍了GEMV2。新版本的一代，评估和指标基准为数据集，模型和指标开发人员提供了模块化基础架构，以使彼此受益。GEMV2支持40种记录的数据集中51种语言。所有数据集的模型都可以在线评估，我们的交互式数据卡创建和渲染工具使得在Living Benchmark中添加新数据集变得更加容易。
translated by 谷歌翻译
我们提出了联合动量对比聚类（FEDMCC），这是一个学习框架，不仅可以在分布式本地数据上提取区分性表示，而且可以执行数据群集。在FEDMCC中，转换的数据对通过在线和目标网络都通过，从而确定了四个表示损失的表示。FEDMCC生成的产生的高质量表示可以胜过几种现有的自制学习方法，用于线性评估和半监督学习任务。FEDMCC可以通过我们称为动量对比聚类（MCC）轻松地适应普通的集中聚类。我们表明，MCC在某些数据集（例如STL-10和Imagenet-10）中实现了最先进的聚类精度。我们还提出了一种减少聚类方案的内存足迹的方法。
translated by 谷歌翻译
熵建模是高性能图像压缩算法的关键组件。自回旋上下文建模的最新发展有助于基于学习的方法超越了经典的方法。但是，由于潜在空间中的空间通道依赖性以及上下文适应性的次优实现，这些模型的性能可以进一步提高。受到变压器的自适应特性的启发，我们提出了一个基于变压器的上下文模型，名为ContextFormer，该模型将事实上的标准注意机制推广到时空通道的注意力。我们用上下文形式替换了现代压缩框架的上下文模型，并在广泛使用的柯达，Clic2020和Tecnick Image数据集上进行测试。我们的实验结果表明，与标准多功能视频编码（VVC）测试模型（VTM）16.2相比，提出的模型可节省多达11％的利率，并且在PSNR和MS-SSIM方面优于各种基于学习的模型。
translated by 谷歌翻译
Covid-19大流行是人类的祸害，宣称全世界超过500万人的生活。虽然疫苗正在全世界分布，但表观需要实惠的筛选技术，以便为无法获得传统医学的世界服务。人工智能可以提供利用咳嗽声音作为主要筛选模式的解决方案。本文介绍了多种模型，这些模型在学术文献目前呈现的最大评估数据集上取得了相对尊敬的性能。此外，我们还显示性能随着培训数据规模而增加，表明世界各地的数据收集，以帮助使用非传统方式对抗Covid-19大流行。
translated by 谷歌翻译
AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。
translated by 谷歌翻译