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These are the user uploaded subtitles that are being translated: 1 00:00:00,420 --> 00:00:05,070 Hello and welcome back to the course on computer vision in today's tutorial We'll talk about the idea 2 00:00:05,070 --> 00:00:06,670 behind Gad's. 3 00:00:06,670 --> 00:00:14,550 Guns were invented by a good fellow or to be entirely correct by Dr good fellow who currently works 4 00:00:14,610 --> 00:00:20,960 at openly as a researcher and he's only 31 years old. 5 00:00:20,970 --> 00:00:27,030 He actually created Gannes even earlier so when he was in in his late 20s a couple of years ago he created 6 00:00:27,030 --> 00:00:34,950 Gans and the story goes that he finished off the idea of guns and when he was at a bar none of that's 7 00:00:34,950 --> 00:00:39,120 true or not but you can definitely check out more of that online. 8 00:00:39,120 --> 00:00:41,690 But it's a crazy story anyway. 9 00:00:42,450 --> 00:00:50,430 How such inside such a young age has brought something to the world that is really drastically changing 10 00:00:50,430 --> 00:00:55,460 this whole field of neural networks and computer vision. 11 00:00:55,560 --> 00:01:02,370 And so what are Gad's the creation grandstands for generative adversarial network. 12 00:01:02,550 --> 00:01:09,840 And we're going to talk more about what Gannes are and how they're shocked at how they're trained what 13 00:01:09,840 --> 00:01:12,640 they're used for in the further trolls in this section. 14 00:01:12,670 --> 00:01:15,080 But I just want to talk about the philosophy. 15 00:01:15,090 --> 00:01:18,110 Why were they created in the first place. 16 00:01:18,360 --> 00:01:27,660 Well the reason why Gannes were constructed is because neural networks are generally good at predicting 17 00:01:27,660 --> 00:01:31,770 things or classifying things and solving problems. 18 00:01:31,800 --> 00:01:41,640 But we wanted to create or I guess to say that researchers AI researchers wanted to create a type of 19 00:01:41,640 --> 00:01:48,220 neural network that we can create for itself and that's why they came up with this idea of generative 20 00:01:48,240 --> 00:01:49,980 adversarial networks. 21 00:01:49,980 --> 00:02:00,090 So Gans can actually generate images they can create images of all kinds of photos of things that never 22 00:02:00,090 --> 00:02:01,980 actually ever existed before. 23 00:02:02,130 --> 00:02:11,620 So they learn about the world they learn about our objects that we have that we use or animals or anything. 24 00:02:11,790 --> 00:02:18,150 And then they can create new versions of those objects that actually never walk this planet or never 25 00:02:18,600 --> 00:02:24,600 these objects never existed were never built by humans but they can create images of those objects so 26 00:02:24,600 --> 00:02:29,010 they can actually if you see you can think about it as they can have an imagination. 27 00:02:29,160 --> 00:02:33,330 And the way that Gannes work is they have two components. 28 00:02:33,330 --> 00:02:36,490 They have a generator and a discriminator. 29 00:02:37,080 --> 00:02:39,960 And these two components are constantly in touch. 30 00:02:40,050 --> 00:02:47,880 The generator generates images and the discriminator then assesses those images and tells the generator 31 00:02:47,880 --> 00:02:56,670 whether or not those images are likely to be similar or are similar to what it has actually seen in 32 00:02:56,670 --> 00:02:59,850 the real world that already exists. 33 00:02:59,850 --> 00:03:02,920 So it's got these two components at work in tandem. 34 00:03:03,060 --> 00:03:05,790 And when you're training this is the interesting part. 35 00:03:05,780 --> 00:03:10,470 This is like a very philosophical part really that will definitely go into the detail about how the 36 00:03:10,470 --> 00:03:13,620 generator works how it works how they connect and so on. 37 00:03:13,620 --> 00:03:20,550 But what we need to understand is the philosophical concept that when you're creating a degenerative 38 00:03:20,640 --> 00:03:28,260 adversarial network when you're training up you are bringing these two up together from scratch so it's 39 00:03:28,260 --> 00:03:29,340 not like you have. 40 00:03:29,400 --> 00:03:35,490 And this is the part where I needed to get my head around when I was learning about them is that you're 41 00:03:35,490 --> 00:03:40,890 not just creating is it's like you have a discriminator that already knows everything about the world. 42 00:03:40,980 --> 00:03:47,730 And then it is this generator is generating things and the discriminator can't right away tell the generator 43 00:03:47,790 --> 00:03:50,710 if that is you know that looks like a real object or not. 44 00:03:50,850 --> 00:03:56,740 No the fact is that they start from scratch from zero and then they learn together. 45 00:03:56,850 --> 00:04:04,860 So the generator generate some images for instance you're generating images of tables and then the discriminator 46 00:04:04,860 --> 00:04:09,580 will look at the images that the generated generated and will look at some images of real tables to 47 00:04:09,630 --> 00:04:16,760 compare the two and it will learn for itself what is a table what isn't a table because it all know 48 00:04:16,770 --> 00:04:22,710 but by default it will know what the generator generates actually doesn't exist in it. 49 00:04:23,010 --> 00:04:29,180 It has that knowledge and therefore it will know what is a table what isn't a table and then it will 50 00:04:29,180 --> 00:04:35,220 also give feedback to the generator saying hey your tables are not really tables because of this and 51 00:04:35,220 --> 00:04:35,730 this and this. 52 00:04:35,730 --> 00:04:36,680 I could tell right away. 53 00:04:36,690 --> 00:04:38,430 So they were kind of playing a game there. 54 00:04:38,490 --> 00:04:41,610 Think of it as are against each other but they're learning together. 55 00:04:41,610 --> 00:04:47,640 So the general is always trying to create images which look like real tables to fool the discriminator 56 00:04:47,640 --> 00:04:52,530 to trick the discriminator in believing that those are real tables whereas the real discrimination always 57 00:04:52,530 --> 00:04:59,780 has actual real to images of actual real tables to compare to in order to learn better. 58 00:04:59,910 --> 00:05:02,340 And then also give feedback to the generator. 59 00:05:02,400 --> 00:05:06,600 And in that sense and we'll see exactly the mechanics of this for it in that sense they learn together 60 00:05:06,600 --> 00:05:17,190 they grow together and eventually this whole network as a whole it learns how to better and better and 61 00:05:17,180 --> 00:05:25,140 better create these tables that are indistinguishable from the real world tables and zone even though 62 00:05:25,160 --> 00:05:30,240 they're working against each other fighting against each other as a whole the network is getting better 63 00:05:30,270 --> 00:05:32,540 and better and better and more robust. 64 00:05:32,580 --> 00:05:43,740 So that's the philosophy behind Gannes that they are designed to create objects or images to be to be 65 00:05:43,980 --> 00:05:51,330 exactly and be entirely accurate images of objects animals or even humans based on what we have in the 66 00:05:51,330 --> 00:05:51,710 real world. 67 00:05:51,710 --> 00:05:58,050 So you can think of it as a general adversarial network who's actually learning about our world and 68 00:05:58,050 --> 00:06:07,680 then creating objects or images of objects which actually are very similar to what we have in our world 69 00:06:07,710 --> 00:06:09,500 but never actually existed. 70 00:06:10,050 --> 00:06:11,400 So yeah that's us. 71 00:06:11,460 --> 00:06:12,510 I hope that sounds exciting. 72 00:06:12,510 --> 00:06:14,780 We're going to delve into that in the upcoming tutorials. 73 00:06:14,880 --> 00:06:21,430 And for now if you'd like some additional reading there's a great blog post by Chan chan SORN. 74 00:06:21,600 --> 00:06:22,550 So torn. 75 00:06:22,590 --> 00:06:24,720 I hope I pronounced that right. 76 00:06:25,290 --> 00:06:27,690 It's on Hakkar noon and you can check it out there. 77 00:06:27,720 --> 00:06:28,800 I've got a link. 78 00:06:28,800 --> 00:06:33,360 Basically it will talk you through the intuition behind Ganns. 79 00:06:33,490 --> 00:06:39,840 Well we're going to discuss all of this in upcoming tutorials but in case you wanted to prepare in case 80 00:06:39,840 --> 00:06:45,720 you wanted to do some pre-reading so that your you know you have an additional point of view then you 81 00:06:45,720 --> 00:06:49,260 can check out some extra insights over there. 82 00:06:49,380 --> 00:06:52,730 But again we will discuss all of this in upcoming tutorials. 83 00:06:52,920 --> 00:06:54,410 And I can't wait to see you there. 84 00:06:54,570 --> 00:06:55,450 Until next time. 85 00:06:55,620 --> 00:06:56,660 Enjoy computer vision. 9634