[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fv_J86z3rSohzJHWefWvF_6b-Uew5071Y8VJHdwg2p3Q":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":14,"related":15,"source":19,"type":20},[],"2025-04-28 17:44:19",1069533158,[8,9],"对","错",{"courseId":11,"workId":12,"workName":13},"1000128447","61792236","第六章单元测试","图像工作流可以一次性生成想要的作品,不需要迭代与优化.( )",[16,21,26,31,41],{"answer":17,"createTime":5,"id":6,"options":18,"question":14,"source":19,"type":20},[],[8,9],"v2",3,{"answer":22,"createTime":5,"id":23,"options":24,"question":25,"source":19,"type":20},[],1069533160,[8,9],"扩散模型是一种基于生成对抗网络的模型.( )",{"answer":27,"createTime":5,"id":28,"options":29,"question":30,"source":19,"type":20},[],1069533162,[8,9],"扩散模型(Diffusion Model)通过逐步加入噪声和去噪过程生成图像,因此生成的图像通常质量较差.( )",{"answer":32,"createTime":5,"id":33,"options":34,"question":39,"source":19,"type":40},[],1069533164,[35,36,37,38],"扩散模型生成图像时通过逐步去噪,而GAN直接生成图像","扩散模型需要较小的训练数据集,而GAN需要大量标注数据","扩散模型使用循环神经网络,而GAN使用卷积神经网络","扩散模型适用于图像分类任务,而GAN仅用于生成任务","扩散模型与GAN的最大区别是: ( )",0,{"answer":42,"createTime":5,"id":43,"options":44,"question":49,"source":19,"type":40},[],1069533166,[45,46,47,48],"创建一个足够强大的判别器","生成逼真的图像以欺骗判别器","生成图像并直接进行分类","最小化图像的噪声","生成对抗网络(GAN)中的生成器的目标是:( )"]