So a non contrastive method, for example, like clustering is one non contrastive method.

因此，比方法之一，例如聚类，便一比方法。

Irregularity was typical of Baroque art, which was much richer in highly contrastive forms than the plainer and more harmonious Renaissance art.

不规则巴洛克艺术的典型特征，与文艺复兴时期的艺术相比，巴洛克艺术在高度比的形式上更为丰富。

As a contrastive body, it is  second in the Solar System only to Saturn's moon Iapetus.

作为一个比天体，它在太阳系中排名第二，仅次于土星的卫星土卫八。

And so what has happened is basically that typically these methods that are contrastive really require access to lots of negatives, which becomes harder and harder to sort of scale when designing a learning algorithm.

因此，通常情况下， 这些比方法确实需要访问大量的负本，而在设计学习算法时， 这变得越来越难以扩展。

(3) Contrastive distribution: If two sounds are regarded as different phonemes, they must be in contrastive distribution.

(3)比分布：如果两个声音被视为不同的音素，则它们一定比分布的。

Compare the unstressed French first person pronoun je (in je sais and j'aime) with the contrastive form moi.

比较无重音法语第一人称代词je(in je sais and j'aime)和比形式moi。

The way most of the current methods work, contrastive or otherwise, is by taking an image, in the case of images, is by taking an image and then computing basically two perturbations of it.

因此，它在计算机视觉， 尤在自监督学习中， 扮演了基础性的角色。 当前大多数方法，无论比学习他方式， 工作原理通常选取一张图片，在处理图像的情况下， 就选取一张图片， 然后基本上计算出它的两扰动版本。 这些扰动可以图像的两个不同裁剪部分， 伴随着不同的光照条件、比度或色彩变化。

And second of all, what's the role of data augmentation in self-supervised learning and contrastive learning?

次，数据增强在自监督学习和比学习中扮演什么角色？

And the idea basically is that you can explain a lot of the contrastive models, GANs, for example, which are like generative adversarial networks.

这个基本思想， 你可以用能量函数来很好地解释许多比模型，比如生成抗网络（GANs）， 它们就像生成抗网络一。

This might be a good place to, you already mentioned it, but what is contrastive learning and what are energy-based models?

这或许个好机会， 你已经提到过，但究竟什么比学习， 什么基于能量的模型呢？

And again, you use contrastive learning for this.

再次，你为此采用比学习方法。

So that's been one of the reasons why non contrastive methods have become like popular and why people think that they're going to be more useful.

这就为何比方法变得流行，并被认为将更加有用的原因之一。

It's just a simple application of contrastive learning.

这只比学习的一个简单应用。

So this is the contrastive part.

所以这就比的部分。

So in contrastive learning, basically the video and the corresponding audio are positives.

因此，在比学习中，基本原则：视频及应的音频被视为正本。

One idea that I proposed in the early 90s with my colleagues at Bell Labs, Jane Bromley and a couple other people, which we now call contrastive learning, which is to have negative examples, right?

我于20世纪90年代初与贝尔实验室的同事们，包括Jane Bromley等人， 共同提出了一理念，如今我们称之为比学习。核心在于引入负本，吧？ 也就说，我们拥有成的已知不同的图像。

So like I said about contrastive learning, you have this notion of a positive and a negative.

就像我之前提到的比学习，它涉及到正本和负本的概念。

Is similarity learning or contrastive learning or these non-contrastive methods the same as understanding something?

相似性学习或比学习或这些比方法就理解某件事吗？

You can do this by contrastive learning.

你可以通过比学习来实现这一点。