论文标题
零情节几乎没有相反的预测性编码：解决智能测试而无需事先培训
Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training
论文作者
论文摘要
视频预测模型通常结合了三个组成部分：从像素空间到小潜伏空间的编码器，潜在空间预测模型以及生成模型回到像素空间。但是，大型且不可预测的像素空间使训练模型变得困难，需要许多培训示例。我们认为，找到一个预测性潜在变量并使用它来评估未来图像的一致性可以实现数据有效的预测，因为它排除了生成模型培训的必要性。为了证明这一点，我们创建了序列完成智能测试，其中该任务是在一系列图像中识别一个可预测的变化功能，并使用此预测来选择后续图像。我们表明，一维的马尔可夫对比预测编码（M-CPC_1D）模型有效地解决了这些测试，只有五个示例。最后，我们证明了M-CPC_1D在未经事先训练的情况下解决两个任务的有用性：异常检测和随机运动视频预测。
Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the large and unpredictable pixel space makes training such models difficult, requiring many training examples. We argue that finding a predictive latent variable and using it to evaluate the consistency of a future image enables data-efficient predictions because it precludes the necessity of a generative model training. To demonstrate it, we created sequence completion intelligence tests in which the task is to identify a predictably changing feature in a sequence of images and use this prediction to select the subsequent image. We show that a one-dimensional Markov Contrastive Predictive Coding (M-CPC_1D) model solves these tests efficiently, with only five examples. Finally, we demonstrate the usefulness of M-CPC_1D in solving two tasks without prior training: anomaly detection and stochastic movement video prediction.
