WEBVTT Kind: captions; Language: en-US NOTE Created on 2024-02-07T21:00:00.0962747Z by ClassTranscribe 00:01:35.120 --> 00:01:36.820 Alright, good morning everybody. 00:01:41.410 --> 00:01:42.020 So. 00:01:42.870 --> 00:01:44.460 And so this is where we are in this 00:01:44.460 --> 00:01:45.100 semester. 00:01:45.790 --> 00:01:49.530 Just to kind of touch base. 00:01:50.420 --> 00:01:51.740 So we finished. 00:01:51.740 --> 00:01:53.770 So we just finished the section on 00:01:53.770 --> 00:01:55.980 supervised learning fundamentals, so. 00:01:57.120 --> 00:01:59.720 Their previous section was basically 00:01:59.720 --> 00:02:01.720 talking about machine learning models 00:02:01.720 --> 00:02:02.480 in general. 00:02:02.480 --> 00:02:04.052 We talked about a big variety of 00:02:04.052 --> 00:02:05.310 machine learning models and the basic 00:02:05.310 --> 00:02:05.970 concepts. 00:02:07.260 --> 00:02:08.910 Your next we're going to talk about the 00:02:08.910 --> 00:02:11.100 application of some of those models, 00:02:11.100 --> 00:02:13.520 particularly deep learning models to 00:02:13.520 --> 00:02:16.510 vision and language primarily first. 00:02:17.720 --> 00:02:19.690 So I'm going to talk about deep 00:02:19.690 --> 00:02:21.820 networks and computer vision today. 00:02:22.620 --> 00:02:24.790 I'll talk about language models like 00:02:24.790 --> 00:02:28.380 how to represent words and probably get 00:02:28.380 --> 00:02:30.580 into Transformers a little bit on 00:02:30.580 --> 00:02:31.120 Tuesday. 00:02:32.000 --> 00:02:34.010 And then talk about the use of 00:02:34.010 --> 00:02:35.690 Transformers, which is just a different 00:02:35.690 --> 00:02:38.490 kind of deep network in language and 00:02:38.490 --> 00:02:40.940 vision on Thursday, the following 00:02:40.940 --> 00:02:41.420 Thursday. 00:02:42.180 --> 00:02:46.260 Then clip and GPT 3, which are two like 00:02:46.260 --> 00:02:50.480 known foundation models that also build 00:02:50.480 --> 00:02:52.260 on the previous concepts. 00:02:53.490 --> 00:02:56.770 And then the so in terms of 00:02:56.770 --> 00:02:58.320 assignments, remember that homework two 00:02:58.320 --> 00:02:59.050 is due on Monday. 00:03:00.110 --> 00:03:03.660 The exam is on March 9th, Thursday, and 00:03:03.660 --> 00:03:04.450 again, you don't. 00:03:04.450 --> 00:03:05.750 You should not come to class. 00:03:05.750 --> 00:03:06.980 I won't be here. 00:03:06.980 --> 00:03:08.050 Nobody needs to be here. 00:03:08.820 --> 00:03:11.570 It's take it's a take home or it's at 00:03:11.570 --> 00:03:14.475 home and it's open book and I'll send 00:03:14.475 --> 00:03:14.760 a. 00:03:16.330 --> 00:03:19.550 Sometime like next week I'll send a 00:03:19.550 --> 00:03:22.230 campus wire message with more details, 00:03:22.230 --> 00:03:23.990 but no new information really. 00:03:26.170 --> 00:03:27.780 So this is the Thursday before spring 00:03:27.780 --> 00:03:29.406 break, then there's spring break. 00:03:29.406 --> 00:03:31.730 Then I'll talk about how we can 00:03:31.730 --> 00:03:34.576 different ways to adapt like networks 00:03:34.576 --> 00:03:38.070 or adapt methods to new domains or new 00:03:38.070 --> 00:03:38.530 tasks. 00:03:40.400 --> 00:03:43.910 Talk about like some of the moral and 00:03:43.910 --> 00:03:48.160 ethical issues that surround AI and 00:03:48.160 --> 00:03:48.510 learning. 00:03:49.680 --> 00:03:50.750 And then? 00:03:50.870 --> 00:03:54.063 And then some issues around data and 00:03:54.063 --> 00:03:54.980 data sets. 00:03:54.980 --> 00:03:57.830 And then the next section is on parent 00:03:57.830 --> 00:03:58.405 discovery. 00:03:58.405 --> 00:04:00.590 So that's focused on unsupervised 00:04:00.590 --> 00:04:03.300 methods where you don't have a ground 00:04:03.300 --> 00:04:05.068 truth for what you're trying to 00:04:05.068 --> 00:04:05.391 predict. 00:04:05.391 --> 00:04:07.420 And you may not be trying to predict 00:04:07.420 --> 00:04:08.910 anything, you may just be organizing 00:04:08.910 --> 00:04:09.723 the data. 00:04:09.723 --> 00:04:12.490 So we'll talk about clustering, missing 00:04:12.490 --> 00:04:14.290 data problems, and the M algorithm. 00:04:15.340 --> 00:04:17.400 How to estimate probabilities? 00:04:17.400 --> 00:04:20.890 Data visualization most likely. 00:04:20.890 --> 00:04:22.810 Not completely sure about these two 00:04:22.810 --> 00:04:25.270 topics yet, but I may do topic modeling 00:04:25.270 --> 00:04:28.150 which is another language technique and 00:04:28.150 --> 00:04:31.180 CCA canonical correlation analysis. 00:04:32.240 --> 00:04:35.210 And then there's going to be more 00:04:35.210 --> 00:04:37.860 applications and special topics. 00:04:37.860 --> 00:04:40.450 So I'm planning to talk about audio. 00:04:40.450 --> 00:04:43.020 I think Josh Levine, one of the Texas, 00:04:43.020 --> 00:04:45.210 is going to talk about reinforcement 00:04:45.210 --> 00:04:45.849 learning, I think. 00:04:46.910 --> 00:04:50.180 And then I'll talk about the difference 00:04:50.180 --> 00:04:52.500 between machine learning practice and 00:04:52.500 --> 00:04:53.770 theory because. 00:04:54.750 --> 00:04:56.160 Kind of like what you focus on is 00:04:56.160 --> 00:04:57.500 pretty different when you go out and 00:04:57.500 --> 00:04:58.910 apply machine learning versus when 00:04:58.910 --> 00:05:00.520 you're trying to develop algorithms or 00:05:00.520 --> 00:05:02.310 you're reading papers, what they're 00:05:02.310 --> 00:05:02.840 focused on. 00:05:04.570 --> 00:05:05.680 And then this will be your wrap up. 00:05:07.650 --> 00:05:09.853 And then it's cut off here, but at the 00:05:09.853 --> 00:05:12.436 if it had more in lines, you would have 00:05:12.436 --> 00:05:15.500 the final project is due on that 00:05:15.500 --> 00:05:16.600 following day. 00:05:16.600 --> 00:05:18.110 And then I think the exam, if I 00:05:18.110 --> 00:05:19.835 remember correctly, is May 9th, the 00:05:19.835 --> 00:05:21.360 final exam which is also. 00:05:22.310 --> 00:05:23.990 Take at Home, open book. 00:05:28.350 --> 00:05:31.020 And I've said it before, but some 00:05:31.020 --> 00:05:34.850 people have been asking you if you need 00:05:34.850 --> 00:05:36.170 to reach like a certain number of 00:05:36.170 --> 00:05:36.820 project points. 00:05:36.820 --> 00:05:38.230 So if you're in the graduate section 00:05:38.230 --> 00:05:39.780 it's now or if you're in the four 00:05:39.780 --> 00:05:42.640 credit section, it's now 525 total 00:05:42.640 --> 00:05:42.930 points. 00:05:43.850 --> 00:05:45.200 If you're in the three credit section, 00:05:45.200 --> 00:05:46.720 it's 425 points. 00:05:47.340 --> 00:05:49.760 And you can make those points out of 00:05:49.760 --> 00:05:51.260 like multiple homeworks or a 00:05:51.260 --> 00:05:52.985 combination of the homework and the 00:05:52.985 --> 00:05:53.680 final project. 00:05:53.680 --> 00:05:57.114 So if you don't want to do the final 00:05:57.114 --> 00:05:59.600 project, you can earn extra points on 00:05:59.600 --> 00:06:01.103 the homeworks and skip the final 00:06:01.103 --> 00:06:01.389 project. 00:06:02.220 --> 00:06:04.210 But you're also welcome to do more 00:06:04.210 --> 00:06:05.580 points than you actually need for the 00:06:05.580 --> 00:06:06.110 grade. 00:06:06.110 --> 00:06:08.102 So like, even if you didn't need to do 00:06:08.102 --> 00:06:10.239 the final project, but if you want to 00:06:10.240 --> 00:06:12.312 because you want to learn, or you want 00:06:12.312 --> 00:06:14.085 to like, get feedback on a project you 00:06:14.085 --> 00:06:15.350 want to do, then you're welcome to do 00:06:15.350 --> 00:06:15.500 that. 00:06:20.070 --> 00:06:20.500 Question. 00:06:36.420 --> 00:06:38.100 You can only use. 00:06:38.100 --> 00:06:39.770 You can only get a total. 00:06:39.770 --> 00:06:41.427 So the question is whether you can get 00:06:41.427 --> 00:06:42.770 extra apply extra credit on the 00:06:42.770 --> 00:06:44.050 projects to the exam. 00:06:44.680 --> 00:06:51.800 You can only apply 15 out of 425 or 525 00:06:51.800 --> 00:06:53.210 to the exam. 00:06:53.210 --> 00:06:53.580 Sort of. 00:06:54.210 --> 00:07:01.010 So if you got 440 points total and 00:07:01.010 --> 00:07:03.190 you're in the three credit version then 00:07:03.190 --> 00:07:06.454 your project score would be 440 / 425 00:07:06.454 --> 00:07:09.786 and so it would be like more than 100%. 00:07:09.786 --> 00:07:11.570 So that's essentially like giving you 00:07:11.570 --> 00:07:12.710 credit towards the exam. 00:07:12.710 --> 00:07:15.340 But I limit it because I don't want 00:07:15.340 --> 00:07:16.618 because the exams complementary. 00:07:16.618 --> 00:07:19.760 So I don't want like somebody to just. 00:07:20.390 --> 00:07:23.190 Do a lot of homework and then bomb the 00:07:23.190 --> 00:07:25.790 exam, because I would still like not be 00:07:25.790 --> 00:07:27.382 very confident that they understand a 00:07:27.382 --> 00:07:28.690 lot of the concepts if they did that. 00:07:32.020 --> 00:07:32.760 OK. 00:07:32.760 --> 00:07:36.500 So I'm going to talk about the Imagenet 00:07:36.500 --> 00:07:38.110 challenge and a little bit more detail, 00:07:38.110 --> 00:07:39.280 which was like really? 00:07:39.990 --> 00:07:42.710 A watershed moment in data set for 00:07:42.710 --> 00:07:43.710 vision and deep learning. 00:07:44.890 --> 00:07:46.820 Then I'll talk, I'll go into more 00:07:46.820 --> 00:07:48.170 detail about the RESNET model. 00:07:49.350 --> 00:07:52.460 I'll talk about how we can adapt pre 00:07:52.460 --> 00:07:54.075 trained network to new tasks, which is 00:07:54.075 --> 00:07:55.270 a really common thing to do. 00:07:56.200 --> 00:07:59.480 And then about the mask R CNN line of 00:07:59.480 --> 00:08:01.650 object detection and segmentation, 00:08:01.650 --> 00:08:04.520 which is a really commonly used system. 00:08:05.430 --> 00:08:07.400 By non vision researchers as well if 00:08:07.400 --> 00:08:08.910 you're trying to detect things in 00:08:08.910 --> 00:08:09.310 images. 00:08:10.010 --> 00:08:12.450 And then very briefly about the unit 00:08:12.450 --> 00:08:13.120 architecture. 00:08:15.820 --> 00:08:18.660 So the Imagenet challenge was really, 00:08:18.660 --> 00:08:20.880 at the time, a very unique data set in 00:08:20.880 --> 00:08:24.362 the scale of the number of classes and 00:08:24.362 --> 00:08:25.990 the number of images that were labeled. 00:08:26.710 --> 00:08:30.150 So there's 20 in total, 22,000 00:08:30.150 --> 00:08:32.690 categories and 15 million images. 00:08:32.690 --> 00:08:35.570 Initially, the challenge was for 1000 00:08:35.570 --> 00:08:38.050 of the categories with a subset of 00:08:38.050 --> 00:08:40.720 those images, but now there's a there's 00:08:40.720 --> 00:08:43.100 also an image net 22K data set that 00:08:43.100 --> 00:08:44.230 people sometimes use. 00:08:45.570 --> 00:08:48.120 For training models as well. 00:08:49.560 --> 00:08:50.890 So how did they get this data? 00:08:51.650 --> 00:08:53.910 So they started with Wordnet to find to 00:08:53.910 --> 00:08:57.573 get a set of nouns that they could that 00:08:57.573 --> 00:08:59.380 they could use for their classes. 00:08:59.380 --> 00:09:03.380 So Wordnet was this was like a graph 00:09:03.380 --> 00:09:04.730 structure of words and their 00:09:04.730 --> 00:09:07.330 relationships that if I remember 00:09:07.330 --> 00:09:08.520 correctly it was from Princeton. 00:09:09.390 --> 00:09:11.850 And so they just basically like mine 00:09:11.850 --> 00:09:13.240 Wordnet ticket. 00:09:14.400 --> 00:09:16.490 To get a bunch of different nouns, 00:09:16.490 --> 00:09:17.125 German shepherd. 00:09:17.125 --> 00:09:19.000 And so they're like descending like 00:09:19.000 --> 00:09:21.205 several levels into the Wordnet tree 00:09:21.205 --> 00:09:23.580 and pulling nouns from there that could 00:09:23.580 --> 00:09:24.720 be visually identified. 00:09:27.990 --> 00:09:31.400 Then for each of those nouns they'll do 00:09:31.400 --> 00:09:34.470 they do a like a Google image search I 00:09:34.470 --> 00:09:37.170 think it was, and download a bunch of 00:09:37.170 --> 00:09:40.245 images that are like the top hits for 00:09:40.245 --> 00:09:40.880 that noun. 00:09:41.660 --> 00:09:44.540 So in consequence, these tend to be 00:09:44.540 --> 00:09:47.130 like pretty like relatively like easy 00:09:47.130 --> 00:09:48.670 examples of those nouns. 00:09:49.540 --> 00:09:51.142 So they for example, when you search 00:09:51.142 --> 00:09:52.710 for German Shepherd, most of them are 00:09:52.710 --> 00:09:54.479 just like pictures of a German shepherd 00:09:54.480 --> 00:09:56.050 rather than pictures that happen to 00:09:56.050 --> 00:09:57.000 have a German shepherd in it. 00:09:58.360 --> 00:10:01.120 But again the aim is to classify is 00:10:01.120 --> 00:10:03.300 going to be classified each image into 00:10:03.300 --> 00:10:04.350 a particular category. 00:10:05.110 --> 00:10:06.500 When you download stuff, you're going 00:10:06.500 --> 00:10:07.850 to also get other random things. 00:10:07.850 --> 00:10:09.660 So they have a Dalmatian here, there's 00:10:09.660 --> 00:10:12.450 a sketch, there's a picture of Germany. 00:10:14.320 --> 00:10:17.110 And so then they need to clean up this 00:10:17.110 --> 00:10:17.495 data. 00:10:17.495 --> 00:10:19.080 So you want to try to, as much as 00:10:19.080 --> 00:10:21.510 possible, remove all the images that 00:10:21.510 --> 00:10:22.880 don't actually correspond to German 00:10:22.880 --> 00:10:23.360 Shepherd. 00:10:25.440 --> 00:10:27.290 So they actually, they tried doing this 00:10:27.290 --> 00:10:27.770 many ways. 00:10:27.770 --> 00:10:29.330 They tried doing it themselves. 00:10:29.330 --> 00:10:31.280 It was just like way too big of a task. 00:10:31.280 --> 00:10:35.280 They tried getting 1000 like people in 00:10:35.280 --> 00:10:36.080 Princeton to do it. 00:10:36.080 --> 00:10:37.020 That was still too big. 00:10:37.760 --> 00:10:40.520 So at the end they used Amazon 00:10:40.520 --> 00:10:42.030 Mechanical Turk which is a service 00:10:42.030 --> 00:10:44.645 where you can upload the data and 00:10:44.645 --> 00:10:47.960 annotation interface and then pay 00:10:47.960 --> 00:10:49.350 people to do the annotation. 00:10:49.970 --> 00:10:53.390 And often it's like pretty cheap. 00:10:53.390 --> 00:10:56.040 So we will talk about this more. 00:10:56.040 --> 00:10:59.160 This is a bit of a not this particular 00:10:59.160 --> 00:11:00.690 instance, but in general using 00:11:00.690 --> 00:11:02.350 Mechanical Turk and other like cheap 00:11:02.350 --> 00:11:03.990 labor services. 00:11:04.720 --> 00:11:07.260 Is like one of the commonly talked 00:11:07.260 --> 00:11:07.840 about like. 00:11:09.240 --> 00:11:11.930 Kind of questionable practices of AI, 00:11:11.930 --> 00:11:13.090 but we'll talk about that in another 00:11:13.090 --> 00:11:13.630 lecture. 00:11:24.980 --> 00:11:27.010 So the question is, what if there? 00:11:27.010 --> 00:11:29.145 What if most of it were bad data? 00:11:29.145 --> 00:11:31.330 I think they would just need to like 00:11:31.330 --> 00:11:32.970 download more images I guess. 00:11:38.160 --> 00:11:40.130 If it's not cleaned properly. 00:11:40.830 --> 00:11:44.012 So then you have what's called if some 00:11:44.012 --> 00:11:46.461 of the labels are, if some of the 00:11:46.461 --> 00:11:48.120 images are wrong that are assigned to a 00:11:48.120 --> 00:11:49.866 label, or some of the wrong labels are 00:11:49.866 --> 00:11:50.529 assigned to images. 00:11:52.300 --> 00:11:54.240 That's usually called label noise, 00:11:54.240 --> 00:11:56.170 where and. 00:11:58.210 --> 00:12:02.440 Some methods will be more harmed by 00:12:02.440 --> 00:12:04.980 that than other methods. 00:12:04.980 --> 00:12:07.700 Often if it's just like 1% of the data 00:12:07.700 --> 00:12:09.070 with a deep network, actually the 00:12:09.070 --> 00:12:11.670 impact won't be that bad, because 00:12:11.670 --> 00:12:12.440 you're not. 00:12:14.030 --> 00:12:16.889 Because it would just be kind of like 00:12:16.890 --> 00:12:17.310 a. 00:12:17.310 --> 00:12:19.560 You could think of it as an irreducible 00:12:19.560 --> 00:12:21.470 error of like 1%. 00:12:21.470 --> 00:12:23.540 And the network is cycling through tons 00:12:23.540 --> 00:12:25.240 of data, so it's not going to overly 00:12:25.240 --> 00:12:27.070 focused on those few examples. 00:12:27.820 --> 00:12:30.200 But for methods boosting that give more 00:12:30.200 --> 00:12:32.060 weight to misclassified examples, that 00:12:32.060 --> 00:12:34.970 can really damage those methods because 00:12:34.970 --> 00:12:37.400 they'll start to focus more on the 00:12:37.400 --> 00:12:38.840 incorrectly labeled examples. 00:12:40.190 --> 00:12:41.580 There's a there's a whole line of 00:12:41.580 --> 00:12:43.990 research or many methods proposed for 00:12:43.990 --> 00:12:45.930 how to like better deal with the label 00:12:45.930 --> 00:12:46.260 noise. 00:12:46.260 --> 00:12:47.210 For example, you can try to 00:12:47.210 --> 00:12:49.110 automatically infer whether something 00:12:49.110 --> 00:12:50.800 is from like a true label distribution 00:12:50.800 --> 00:12:52.980 or a noisy label distribution. 00:12:56.440 --> 00:12:58.970 So at the end they get they had 49,000 00:12:58.970 --> 00:13:02.010 workers from 167 different countries 00:13:02.010 --> 00:13:03.380 that contributed to the labeling. 00:13:06.620 --> 00:13:11.820 And then the task is you have to be you 00:13:11.820 --> 00:13:14.140 get to your classifier can make its top 00:13:14.140 --> 00:13:15.310 five predictions. 00:13:16.090 --> 00:13:18.480 And at least one of those predictions 00:13:18.480 --> 00:13:20.300 has to match the ground truth label. 00:13:21.310 --> 00:13:23.170 And the reason for that is that these 00:13:23.170 --> 00:13:24.970 images will often have like multiple 00:13:24.970 --> 00:13:26.810 categories depicted in them. 00:13:26.810 --> 00:13:29.350 So like this has this image has a 00:13:29.350 --> 00:13:30.560 T-shirt and. 00:13:31.320 --> 00:13:34.525 It has a drumstick and it has a steel 00:13:34.525 --> 00:13:36.310 drum, so it's reasonable that the 00:13:36.310 --> 00:13:38.100 classifier could predict those things 00:13:38.100 --> 00:13:38.730 as well. 00:13:38.730 --> 00:13:40.407 But it was supposed to be a picture of 00:13:40.407 --> 00:13:41.000 a steel drum. 00:13:41.780 --> 00:13:44.390 So if your output is scale, T-shirt, 00:13:44.390 --> 00:13:46.400 steel drum, drumstick, mud turtle, then 00:13:46.400 --> 00:13:48.050 it would be considered correct because 00:13:48.050 --> 00:13:50.030 if those are your top five scoring 00:13:50.030 --> 00:13:50.580 classes. 00:13:51.300 --> 00:13:52.900 Because one of them is the ground truth 00:13:52.900 --> 00:13:54.580 label, which is steel drum. 00:13:54.580 --> 00:13:56.530 But if you replace steel drum with 00:13:56.530 --> 00:13:57.866 giant panda, then you're out. 00:13:57.866 --> 00:13:59.250 Then you would be incorrect. 00:13:59.250 --> 00:14:00.970 Because you don't, your top five 00:14:00.970 --> 00:14:02.300 predictions don't include the ground 00:14:02.300 --> 00:14:02.490 truth. 00:14:03.220 --> 00:14:04.940 So a lot of times with Imagenet you'll 00:14:04.940 --> 00:14:07.250 see like top five error which is this 00:14:07.250 --> 00:14:10.215 measure and sometimes you see top one 00:14:10.215 --> 00:14:12.050 measure which is that it has to 00:14:12.050 --> 00:14:13.950 actually predict steel drum as the top 00:14:13.950 --> 00:14:14.320 one hit. 00:14:16.040 --> 00:14:17.450 And yeah, so. 00:14:18.670 --> 00:14:20.840 So it's not it's not possible to get 00:14:20.840 --> 00:14:22.625 perfect accuracy at the top one measure 00:14:22.625 --> 00:14:25.360 for sure, because this could have been 00:14:25.360 --> 00:14:27.610 like a return for T-shirt or something 00:14:27.610 --> 00:14:28.710 you don't really know. 00:14:31.990 --> 00:14:34.635 And then as I mentioned before, like 00:14:34.635 --> 00:14:37.060 the performance of deep networks, this 00:14:37.060 --> 00:14:41.230 is Alex net on Imagenet is what was the 00:14:41.230 --> 00:14:43.425 first like really compelling proof of 00:14:43.425 --> 00:14:45.490 the effectiveness of deep networks? 00:14:47.210 --> 00:14:48.950 Envision, but also in general. 00:14:50.600 --> 00:14:55.707 And the method that performs so in the 00:14:55.707 --> 00:14:57.890 2012 channel was this one, which I've 00:14:57.890 --> 00:14:59.030 already talked about in length. 00:14:59.030 --> 00:14:59.260 So. 00:15:00.210 --> 00:15:01.150 More of a reminder. 00:15:02.790 --> 00:15:04.480 Then I would say the next big 00:15:04.480 --> 00:15:05.230 breakthrough. 00:15:05.230 --> 00:15:06.300 There were a bunch of different 00:15:06.300 --> 00:15:08.470 networks architecture modifications 00:15:08.470 --> 00:15:09.450 that were proposed. 00:15:09.450 --> 00:15:13.750 VGG from Oxford and Googlenet and 00:15:13.750 --> 00:15:15.900 Inception Network and they all had 00:15:15.900 --> 00:15:17.700 their own kinds of innovations and ways 00:15:17.700 --> 00:15:19.320 of trying to make the network deeper. 00:15:19.320 --> 00:15:20.740 But I would say the next major 00:15:20.740 --> 00:15:23.070 breakthrough was Resnet. 00:15:23.070 --> 00:15:25.380 You still see Resnet models used quite 00:15:25.380 --> 00:15:26.760 frequently today. 00:15:28.310 --> 00:15:30.260 So and again in Resnet, the idea is 00:15:30.260 --> 00:15:34.400 that you simply add your if you pass 00:15:34.400 --> 00:15:37.440 the data through a bunch of MLPS or 00:15:37.440 --> 00:15:38.450 convolutional layers. 00:15:39.450 --> 00:15:41.370 But then every couple layers that you 00:15:41.370 --> 00:15:43.710 pass it through, you add back the 00:15:43.710 --> 00:15:45.620 previous value of the features to the 00:15:45.620 --> 00:15:46.050 output. 00:15:46.850 --> 00:15:48.320 And this thing is called a skip 00:15:48.320 --> 00:15:50.330 connection this like identity. 00:15:50.330 --> 00:15:53.690 So it's just you add to the output of a 00:15:53.690 --> 00:15:55.730 couple of layers that are processing X. 00:15:56.510 --> 00:15:58.985 And then this gradient is 1 S it allows 00:15:58.985 --> 00:16:00.940 the gradients to flow very effectively 00:16:00.940 --> 00:16:01.730 through the network. 00:16:03.330 --> 00:16:05.010 There's another variant of this that I 00:16:05.010 --> 00:16:07.210 didn't really have time to get to when 00:16:07.210 --> 00:16:08.580 I was last talking about resets. 00:16:09.440 --> 00:16:11.800 And that's the Resnet bottleneck 00:16:11.800 --> 00:16:12.530 module. 00:16:12.530 --> 00:16:14.170 So this is used for a much deeper 00:16:14.170 --> 00:16:14.740 networks. 00:16:15.720 --> 00:16:18.115 And the idea is that if you have you 00:16:18.115 --> 00:16:21.350 have some like high dimensional feature 00:16:21.350 --> 00:16:24.610 image that's being passed in, so you 00:16:24.610 --> 00:16:27.210 have like for each position it might be 00:16:27.210 --> 00:16:30.710 like a for example like a 14 by 14 grid 00:16:30.710 --> 00:16:33.820 of features and the features are 256 00:16:33.820 --> 00:16:34.720 dimensions deep. 00:16:36.290 --> 00:16:38.380 If you were to do convolution directly 00:16:38.380 --> 00:16:40.390 in that high dimensional feature space, 00:16:40.390 --> 00:16:41.700 then you would need a lot of weights 00:16:41.700 --> 00:16:42.890 and a lot of operations. 00:16:43.930 --> 00:16:45.910 And so the idea is that you first like 00:16:45.910 --> 00:16:47.450 project it down into a lower 00:16:47.450 --> 00:16:50.020 dimensional feature space by taking 00:16:50.020 --> 00:16:52.430 each feature cell by itself each 00:16:52.430 --> 00:16:55.260 position, and you map from 256 00:16:55.260 --> 00:16:57.948 dimensions down to 64 dimensions, so by 00:16:57.948 --> 00:16:59.070 a factor of 4. 00:16:59.850 --> 00:17:01.626 Then you apply the convolution, so then 00:17:01.626 --> 00:17:04.170 you have a filter that's operating over 00:17:04.170 --> 00:17:05.320 the. 00:17:05.700 --> 00:17:08.010 64 deep feature map. 00:17:09.050 --> 00:17:11.560 And then you and then you again like 00:17:11.560 --> 00:17:13.530 take each of those feature cells and 00:17:13.530 --> 00:17:16.480 map them back up to 256 dimensions. 00:17:16.480 --> 00:17:18.690 So this is called a bottleneck because 00:17:18.690 --> 00:17:20.580 you take the feature, the number of 00:17:20.580 --> 00:17:23.210 features, and at each position. 00:17:23.790 --> 00:17:26.303 And you it by a factor of four, and 00:17:26.303 --> 00:17:28.460 then you process it, and then you bring 00:17:28.460 --> 00:17:30.140 it back up to the original 00:17:30.140 --> 00:17:30.950 dimensionality. 00:17:32.730 --> 00:17:34.590 And the main reason for this is that it 00:17:34.590 --> 00:17:37.460 makes things much faster and reduces 00:17:37.460 --> 00:17:38.590 the number of parameters in your 00:17:38.590 --> 00:17:38.980 network. 00:17:39.590 --> 00:17:41.020 So if you were directly doing 00:17:41.020 --> 00:17:45.000 convolution on a 256 dimensional 00:17:45.000 --> 00:17:45.650 feature map. 00:17:46.730 --> 00:17:49.730 Then it would your filter size or the 00:17:49.730 --> 00:17:54.520 filter size would be 256 by three by 00:17:54.520 --> 00:17:55.220 three. 00:17:56.090 --> 00:17:57.970 And the number of operations that you 00:17:57.970 --> 00:17:59.370 would have to do at every position 00:17:59.370 --> 00:18:03.099 would be 256 by 256 by three by three. 00:18:04.010 --> 00:18:06.336 Where if you do this bottleneck, then 00:18:06.336 --> 00:18:08.575 you first have to you first like reduce 00:18:08.575 --> 00:18:10.490 the dimensionality of the features at 00:18:10.490 --> 00:18:11.190 each position. 00:18:11.190 --> 00:18:13.400 So for each position that's going to be 00:18:13.400 --> 00:18:15.620 256 by 64. 00:18:16.810 --> 00:18:19.970 And then you do convolution over the 00:18:19.970 --> 00:18:22.480 image, which for each position will be 00:18:22.480 --> 00:18:25.610 64 by 64 by three by three, because now 00:18:25.610 --> 00:18:27.592 it's only a 64 dimensional feature. 00:18:27.592 --> 00:18:30.050 And then you increase the 00:18:30.050 --> 00:18:32.120 dimensionality again by mapping it back 00:18:32.120 --> 00:18:33.250 up to 256. 00:18:34.000 --> 00:18:36.293 So that's going to be 64 by 256 00:18:36.293 --> 00:18:36.896 operations. 00:18:36.896 --> 00:18:39.730 So it's roughly like 1 ninth as many 00:18:39.730 --> 00:18:40.420 operations. 00:18:41.050 --> 00:18:42.620 And their experiments show that this 00:18:42.620 --> 00:18:45.870 performs very similarly to not doing 00:18:45.870 --> 00:18:48.170 the bottleneck, so it's kind of like a 00:18:48.170 --> 00:18:49.620 free efficiency gain. 00:18:52.170 --> 00:18:52.690 Question. 00:18:58.460 --> 00:18:59.455 Yeah, that's a good question. 00:18:59.455 --> 00:19:01.660 So this is, it's just an MLP, so you 00:19:01.660 --> 00:19:04.590 have like 256 dimensional vector coming 00:19:04.590 --> 00:19:04.800 in. 00:19:05.390 --> 00:19:08.820 And then you have 64 nodes in your 00:19:08.820 --> 00:19:09.200 layer. 00:19:09.870 --> 00:19:11.890 And then so then it just has 64 00:19:11.890 --> 00:19:12.340 outputs. 00:19:15.050 --> 00:19:16.880 And what's not like soup? 00:19:16.880 --> 00:19:21.340 What may not be super super obvious 00:19:21.340 --> 00:19:23.360 about this is you've got like a feature 00:19:23.360 --> 00:19:25.209 map, so you've got some grid of cells 00:19:25.210 --> 00:19:26.390 for each of those cells. 00:19:26.390 --> 00:19:28.470 It's like a vector that's 256 00:19:28.470 --> 00:19:29.780 dimensions long. 00:19:30.570 --> 00:19:32.780 And then you apply this MLP to each of 00:19:32.780 --> 00:19:35.770 those cells separately to map each 00:19:35.770 --> 00:19:39.150 position in your feature map down to 64 00:19:39.150 --> 00:19:39.620 dimensions. 00:19:44.630 --> 00:19:47.820 So all of this allows just one second. 00:19:47.820 --> 00:19:49.930 All of this allows Resnet to go super 00:19:49.930 --> 00:19:50.280 deep. 00:19:50.280 --> 00:19:54.340 So Alex Net was a winner of 2012, then 00:19:54.340 --> 00:19:56.539 VGG was the winner of 2014. 00:19:57.370 --> 00:19:59.520 With 19 layers and then Resnet was the 00:19:59.520 --> 00:20:02.235 winner of 2015 one year later with 152 00:20:02.235 --> 00:20:02.660 layers. 00:20:03.410 --> 00:20:07.792 And these skip connections allow the 00:20:07.792 --> 00:20:09.725 gradient to flow directly, essentially 00:20:09.725 --> 00:20:11.290 to any part of this network. 00:20:11.290 --> 00:20:13.092 Because these are all like gradient of 00:20:13.092 --> 00:20:15.510 1 S, the error gradient can flow to 00:20:15.510 --> 00:20:17.390 everything, and essentially you can 00:20:17.390 --> 00:20:18.620 optimize all these blocks 00:20:18.620 --> 00:20:19.560 simultaneously. 00:20:20.990 --> 00:20:23.280 They also makes the Deep Networks Act 00:20:23.280 --> 00:20:25.320 as a kind of ensemble, where like each 00:20:25.320 --> 00:20:27.995 of these little modules can make their 00:20:27.995 --> 00:20:30.840 own hypothesis or own scores that then 00:20:30.840 --> 00:20:31.763 get added together. 00:20:31.763 --> 00:20:34.420 And you see a kind of ensemble behavior 00:20:34.420 --> 00:20:36.220 in Resnet S and that their variants 00:20:36.220 --> 00:20:38.269 tends to actually decrease as you get 00:20:38.270 --> 00:20:40.020 deeper networks rather than increase, 00:20:40.020 --> 00:20:41.340 which is what you would expect with the 00:20:41.340 --> 00:20:42.420 increased number of parameters. 00:20:43.330 --> 00:20:44.010 Was there a question? 00:20:46.930 --> 00:20:47.210 OK. 00:21:05.550 --> 00:21:07.750 So after. 00:21:08.670 --> 00:21:11.350 So let's say that you're a 14 by 14 00:21:11.350 --> 00:21:14.750 spatially feature map that is 256 00:21:14.750 --> 00:21:18.030 dimensions deep, so 256 features at 00:21:18.030 --> 00:21:20.190 each position in this 14 by 14 map. 00:21:21.430 --> 00:21:23.700 So first this will convert it into a 14 00:21:23.700 --> 00:21:25.700 by 14 by 64. 00:21:26.930 --> 00:21:29.905 Feature map so 14 by 14 spatially, but 00:21:29.905 --> 00:21:31.410 64 dimensions long. 00:21:31.410 --> 00:21:32.770 The feet each feature vector at each 00:21:32.770 --> 00:21:33.230 position. 00:21:33.940 --> 00:21:35.370 Then you can do this three by three 00:21:35.370 --> 00:21:36.897 convolution, which means you have a 00:21:36.897 --> 00:21:38.250 three by three filter. 00:21:38.250 --> 00:21:40.440 That's like has 64 features that's 00:21:40.440 --> 00:21:41.910 operating over that map. 00:21:41.910 --> 00:21:45.860 That doesn't change the size of the 00:21:45.860 --> 00:21:48.165 representation at all, so the output of 00:21:48.165 --> 00:21:51.780 this will still be 14 by 14 by 64. 00:21:53.040 --> 00:21:54.890 Then you apply this at each feature 00:21:54.890 --> 00:21:58.640 cell and this will be a 256 node MLP 00:21:58.640 --> 00:22:00.700 that connects to the 64 features. 00:22:01.590 --> 00:22:05.040 And so this will map that into a 14 by 00:22:05.040 --> 00:22:06.540 14 by 256. 00:22:07.370 --> 00:22:10.089 And so you had a 14 by 14 by 256 that 00:22:10.090 --> 00:22:12.260 was fed in here and then that gets 00:22:12.260 --> 00:22:15.644 added back to the output which is 14 by 00:22:15.644 --> 00:22:16.569 14 by 256. 00:22:20.490 --> 00:22:22.280 There are three by three means that 00:22:22.280 --> 00:22:24.860 it's a convolutional filter, so it has 00:22:24.860 --> 00:22:26.665 three by three spatial extent. 00:22:26.665 --> 00:22:27.480 So the. 00:22:27.480 --> 00:22:31.584 So this will be like operate on it will 00:22:31.584 --> 00:22:31.826 be. 00:22:31.826 --> 00:22:33.120 It's a linear model. 00:22:33.760 --> 00:22:36.280 That operates on the features at each 00:22:36.280 --> 00:22:38.320 position and the features of its 00:22:38.320 --> 00:22:41.610 neighbors, the three the cells that are 00:22:41.610 --> 00:22:42.240 right around it. 00:22:43.670 --> 00:22:45.660 Where these ones, the one by ones, just 00:22:45.660 --> 00:22:47.560 operate at each position without 00:22:47.560 --> 00:22:48.380 considering the neighbors. 00:22:53.820 --> 00:22:55.095 So I'll show you some. 00:22:55.095 --> 00:22:56.700 I'll show some of the architecture 00:22:56.700 --> 00:22:58.390 examples later too, which might also 00:22:58.390 --> 00:22:59.350 help clarify. 00:22:59.350 --> 00:23:00.800 But did you have a question? 00:23:16.600 --> 00:23:18.140 So one clarification. 00:23:18.140 --> 00:23:21.370 Is that a Resnet, you train it with SGD 00:23:21.370 --> 00:23:23.490 still, so there's an optimization 00:23:23.490 --> 00:23:25.110 algorithm and there's an architecture? 00:23:25.850 --> 00:23:29.852 Their architecture defines like how the 00:23:29.852 --> 00:23:31.580 representation will change as you move 00:23:31.580 --> 00:23:33.120 through the network, and the 00:23:33.120 --> 00:23:35.170 optimization defines like how you're 00:23:35.170 --> 00:23:38.030 going to learn the weights that produce 00:23:38.030 --> 00:23:38.960 the representation. 00:23:39.640 --> 00:23:41.210 So the way that you would train a 00:23:41.210 --> 00:23:43.446 Resnet is the exact same as the way 00:23:43.446 --> 00:23:44.980 that you would train Alex net. 00:23:44.980 --> 00:23:47.500 It would still be using SGD like SGD 00:23:47.500 --> 00:23:49.420 with momentum or atom most likely. 00:23:50.990 --> 00:23:53.560 But the architecture is different and 00:23:53.560 --> 00:23:56.140 actually even though this network looks 00:23:56.140 --> 00:23:56.660 small. 00:23:57.500 --> 00:23:59.880 It's actually pretty heavy in the sense 00:23:59.880 --> 00:24:02.055 that it has a lot of weights because it 00:24:02.055 --> 00:24:03.280 has larger filters. 00:24:04.020 --> 00:24:05.230 And it has. 00:24:06.160 --> 00:24:09.776 It has like deeper like this big dense. 00:24:09.776 --> 00:24:11.840 This dense means that linear layer 00:24:11.840 --> 00:24:12.370 essentially. 00:24:12.370 --> 00:24:15.030 So you have this big like 22048 by 00:24:15.030 --> 00:24:15.850 2048. 00:24:17.380 --> 00:24:18.780 Weight matrix here. 00:24:19.440 --> 00:24:21.100 So Alex net actually has a lot of 00:24:21.100 --> 00:24:23.340 parameters and Resnet S are actually 00:24:23.340 --> 00:24:24.730 faster to train. 00:24:24.910 --> 00:24:25.430 00:24:26.030 --> 00:24:28.265 Then these other networks, especially 00:24:28.265 --> 00:24:32.400 then VDG because they're just like 00:24:32.400 --> 00:24:34.710 better suited to optimization. 00:24:35.290 --> 00:24:36.720 So they're still using the same 00:24:36.720 --> 00:24:38.690 optimization method, but the 00:24:38.690 --> 00:24:41.620 architecture makes those methods more 00:24:41.620 --> 00:24:42.710 effective optimizers. 00:24:46.830 --> 00:24:49.910 So there's just a few components in a 00:24:49.910 --> 00:24:52.213 resnet for CNN. 00:24:52.213 --> 00:24:54.470 CNN stands for convolutional neural 00:24:54.470 --> 00:24:56.150 network, so basically where you're 00:24:56.150 --> 00:24:58.890 operating over multiple positions in a 00:24:58.890 --> 00:24:59.170 grid. 00:25:00.620 --> 00:25:02.640 So you first you have these learned 2D 00:25:02.640 --> 00:25:04.680 convolutional features which are 00:25:04.680 --> 00:25:07.420 applying some linear weights to each 00:25:07.420 --> 00:25:08.570 position and its neighbors. 00:25:09.790 --> 00:25:12.770 And then a same size like feature map 00:25:12.770 --> 00:25:13.320 as an output. 00:25:14.660 --> 00:25:15.220 00:25:15.870 --> 00:25:17.900 Then you have what's called batch norm, 00:25:17.900 --> 00:25:19.020 which I'll talk about in the next 00:25:19.020 --> 00:25:19.400 slide. 00:25:20.110 --> 00:25:21.650 And then you have Velu, which we've 00:25:21.650 --> 00:25:23.135 talked about and then linear layers, 00:25:23.135 --> 00:25:26.990 which is MLP, just a perceptron layer. 00:25:30.040 --> 00:25:33.190 So batch normalization is used almost 00:25:33.190 --> 00:25:34.050 all the time now. 00:25:34.050 --> 00:25:36.334 It's a really common, commonly used. 00:25:36.334 --> 00:25:39.000 It's used for vision, but also for 00:25:39.000 --> 00:25:39.870 other applications. 00:25:41.060 --> 00:25:43.460 The main idea of batch norm is that. 00:25:44.140 --> 00:25:45.760 As you're training the network since 00:25:45.760 --> 00:25:47.180 all the weights are being updated. 00:25:47.930 --> 00:25:50.413 The kind of distribution of the 00:25:50.413 --> 00:25:52.060 features is going to keep changing. 00:25:52.060 --> 00:25:55.740 So the features may become like more. 00:25:55.740 --> 00:25:57.790 The mean of the features may change, 00:25:57.790 --> 00:25:59.860 their variance may change because all 00:25:59.860 --> 00:26:00.890 the weights are in flux. 00:26:01.560 --> 00:26:03.730 And so this makes it kind of unstable 00:26:03.730 --> 00:26:05.750 that like the later later layers keep 00:26:05.750 --> 00:26:07.500 on having to adapt to changes in the 00:26:07.500 --> 00:26:09.387 features of the earlier layers, and so 00:26:09.387 --> 00:26:10.740 like all the different layers are 00:26:10.740 --> 00:26:12.910 trying to like improve themselves but 00:26:12.910 --> 00:26:15.120 also react to the improvements of the 00:26:15.120 --> 00:26:16.280 other layers around them. 00:26:18.310 --> 00:26:20.385 The idea of batch norm is. 00:26:20.385 --> 00:26:23.700 So first you can kind of stabilize 00:26:23.700 --> 00:26:27.060 things by subtracting off the and 00:26:27.060 --> 00:26:29.170 dividing by the standard deviation of 00:26:29.170 --> 00:26:31.447 the features within each batch. 00:26:31.447 --> 00:26:33.970 So you could say like, I'm going to do 00:26:33.970 --> 00:26:34.120 this. 00:26:34.770 --> 00:26:37.600 Over the I'm going to subtract the mean 00:26:37.600 --> 00:26:39.190 and divide by the standard deviation of 00:26:39.190 --> 00:26:41.450 the features in the entire data set, 00:26:41.450 --> 00:26:42.870 but that would be really slow because 00:26:42.870 --> 00:26:44.380 you'd have to keep on reprocessing the 00:26:44.380 --> 00:26:46.030 whole data set to get these means in 00:26:46.030 --> 00:26:46.900 steering deviations. 00:26:47.530 --> 00:26:49.405 So instead they say for every batch. 00:26:49.405 --> 00:26:52.560 So you might be processing 120 examples 00:26:52.560 --> 00:26:53.050 at a time. 00:26:53.710 --> 00:26:55.210 You're going to compute the mean of the 00:26:55.210 --> 00:26:57.070 features of that batch, subtract it 00:26:57.070 --> 00:26:58.480 from the original value, compute the 00:26:58.480 --> 00:27:00.060 steering deviation or variance, and 00:27:00.060 --> 00:27:01.670 divide by the standard deviation. 00:27:03.320 --> 00:27:04.570 And then you get your normalized 00:27:04.570 --> 00:27:04.940 feature. 00:27:05.870 --> 00:27:08.325 And then you could say maybe this isn't 00:27:08.325 --> 00:27:09.960 the ideal thing to do. 00:27:09.960 --> 00:27:11.730 Maybe instead you should be using some 00:27:11.730 --> 00:27:13.850 other statistics to shift the features 00:27:13.850 --> 00:27:17.640 or to rescale them, maybe based on a 00:27:17.640 --> 00:27:22.080 longer history and so you have so first 00:27:22.080 --> 00:27:24.640 like you get some features X that are 00:27:24.640 --> 00:27:25.900 passed into the batch norm. 00:27:26.570 --> 00:27:28.715 It computes the mean, computes the 00:27:28.715 --> 00:27:30.410 variance, or equivalently, the square 00:27:30.410 --> 00:27:32.490 of this is steering deviation, 00:27:32.490 --> 00:27:35.020 subtracts the mean from the data, 00:27:35.020 --> 00:27:38.290 divides by the stern deviation, or like 00:27:38.290 --> 00:27:39.660 square root of variance plus some 00:27:39.660 --> 00:27:40.066 epsilon. 00:27:40.066 --> 00:27:41.900 This is just so you don't have a divide 00:27:41.900 --> 00:27:42.420 by zero. 00:27:43.630 --> 00:27:44.870 And. 00:27:45.670 --> 00:27:48.820 And then you get your zero mean unit 00:27:48.820 --> 00:27:50.740 STD normalized features. 00:27:50.740 --> 00:27:52.020 So that's a really common kind of 00:27:52.020 --> 00:27:52.950 normalization, right? 00:27:53.670 --> 00:27:56.420 And then the final output is just some 00:27:56.420 --> 00:28:00.390 gamma times the normalized X plus beta, 00:28:00.390 --> 00:28:00.860 so. 00:28:01.490 --> 00:28:04.520 This allows it to reset, and gamma and 00:28:04.520 --> 00:28:06.170 beta here are learned parameters. 00:28:06.170 --> 00:28:09.130 So this allows it to like adjust the 00:28:09.130 --> 00:28:12.460 shift and adjust the scaling if it's 00:28:12.460 --> 00:28:14.060 like learned to be effective. 00:28:14.060 --> 00:28:17.340 So if gamma is 1 and beta is 0, then 00:28:17.340 --> 00:28:20.820 this would just be a subtracting the 00:28:20.820 --> 00:28:22.290 mean and dividing by steering deviation 00:28:22.290 --> 00:28:23.180 of each batch. 00:28:23.180 --> 00:28:25.136 But it doesn't necessarily have to be 00:28:25.136 --> 00:28:25.309 0. 00:28:26.890 --> 00:28:30.136 And this is showing that how fast this 00:28:30.136 --> 00:28:31.870 is showing the accuracy of some 00:28:31.870 --> 00:28:32.400 training. 00:28:33.120 --> 00:28:36.560 With batch norm and without batch norm 00:28:36.560 --> 00:28:38.850 for a certain number of steps or like 00:28:38.850 --> 00:28:39.530 batches. 00:28:39.530 --> 00:28:41.830 And you can see that with batch norm it 00:28:41.830 --> 00:28:43.820 converges like incredibly faster, 00:28:43.820 --> 00:28:44.190 right? 00:28:44.190 --> 00:28:46.200 So they both get there eventually, but. 00:28:46.880 --> 00:28:49.820 But without batch norm, it takes like 00:28:49.820 --> 00:28:54.040 maybe 20 or 30,000 batches before it 00:28:54.040 --> 00:28:57.870 can start to catch up to what the width 00:28:57.870 --> 00:28:59.990 batch norm processed could do in a 00:28:59.990 --> 00:29:01.590 couple of 1000 iterations. 00:29:02.830 --> 00:29:04.200 And then this thing is showing. 00:29:05.180 --> 00:29:08.920 How the median and the 85th and 15th 00:29:08.920 --> 00:29:10.910 percentile of values are for some 00:29:10.910 --> 00:29:12.670 feature overtime? 00:29:13.290 --> 00:29:15.720 And without batch norm, it's kind of 00:29:15.720 --> 00:29:17.950 like unstable, like sometimes the mean 00:29:17.950 --> 00:29:20.380 shifts away from zero and you get 00:29:20.380 --> 00:29:22.430 increase or decrease the variance, but 00:29:22.430 --> 00:29:24.270 the batch norm results in it being much 00:29:24.270 --> 00:29:25.450 more stable. 00:29:25.450 --> 00:29:27.150 So it slowly increases the variance 00:29:27.150 --> 00:29:29.820 over time and the mean stays at roughly 00:29:29.820 --> 00:29:30.170 0. 00:29:36.620 --> 00:29:39.640 So this is in code what a res block 00:29:39.640 --> 00:29:40.260 looks like. 00:29:41.460 --> 00:29:45.620 So in these N torch you define like you 00:29:45.620 --> 00:29:47.210 always use specify. 00:29:48.050 --> 00:29:49.700 Your network out of these different 00:29:49.700 --> 00:29:51.910 components and then you say like how 00:29:51.910 --> 00:29:53.440 the data will pass through these 00:29:53.440 --> 00:29:53.830 components. 00:29:54.570 --> 00:29:57.485 So a rez block is like 1 section of 00:29:57.485 --> 00:30:00.160 that neural network that has one skip 00:30:00.160 --> 00:30:00.810 connection. 00:30:01.670 --> 00:30:03.070 So first I would start with the 00:30:03.070 --> 00:30:03.720 forward. 00:30:03.720 --> 00:30:05.770 So this says how the data will be 00:30:05.770 --> 00:30:07.040 passed through the network. 00:30:07.040 --> 00:30:09.404 So you compute some shortcut, could 00:30:09.404 --> 00:30:11.180 just be the input. 00:30:11.180 --> 00:30:12.680 I'll get to some detail about that 00:30:12.680 --> 00:30:14.270 later, but let's for now just say you 00:30:14.270 --> 00:30:14.930 have the input. 00:30:16.060 --> 00:30:17.690 Then you pass the input through a 00:30:17.690 --> 00:30:19.710 convolutional layer, through batch 00:30:19.710 --> 00:30:21.270 norm, and through Relu. 00:30:22.470 --> 00:30:25.440 Then you pass that through another 00:30:25.440 --> 00:30:27.930 convolutional layer, through batch norm 00:30:27.930 --> 00:30:28.860 and through railu. 00:30:29.940 --> 00:30:33.800 And then you add the input back to the 00:30:33.800 --> 00:30:36.920 output, and that's your final output 00:30:36.920 --> 00:30:37.800 through summary loop. 00:30:38.760 --> 00:30:40.515 So it's pretty simple. 00:30:40.515 --> 00:30:43.665 It's a convolutional batch norm Relu, 00:30:43.665 --> 00:30:46.080 convolutional batch Norm Relu, add back 00:30:46.080 --> 00:30:48.630 the input and apply one more value. 00:30:49.760 --> 00:30:52.380 And then this is defining the details 00:30:52.380 --> 00:30:54.180 of what these like convolutional layers 00:30:54.180 --> 00:30:54.460 are. 00:30:55.580 --> 00:30:58.280 So first like you can the rez block can 00:30:58.280 --> 00:30:59.590 downsample or not. 00:30:59.590 --> 00:31:01.160 So if it down samples, it means that 00:31:01.160 --> 00:31:04.278 you would go from a 14 by 14 feature 00:31:04.278 --> 00:31:06.949 map into a 7 by 7 feature map. 00:31:07.570 --> 00:31:09.630 So typically in these networks, as I'll 00:31:09.630 --> 00:31:12.990 show in a later slide, you tend to 00:31:12.990 --> 00:31:14.870 start with like a very big feature map. 00:31:14.870 --> 00:31:17.640 It's like image size and then you make 00:31:17.640 --> 00:31:19.846 it smaller and smaller spatially, but 00:31:19.846 --> 00:31:21.270 deeper and deeper in terms of the 00:31:21.270 --> 00:31:21.790 features. 00:31:22.440 --> 00:31:24.910 So that instead of representing like 00:31:24.910 --> 00:31:27.400 very weak features at each pixel 00:31:27.400 --> 00:31:29.808 initially you have an RGB value at each 00:31:29.808 --> 00:31:30.134 pixel. 00:31:30.134 --> 00:31:31.650 You start representing really 00:31:31.650 --> 00:31:33.830 complicated features, but with less 00:31:33.830 --> 00:31:35.350 like spatial definition. 00:31:38.620 --> 00:31:41.870 So if you downsample then instead. 00:31:41.870 --> 00:31:43.800 So let me start with not downsample. 00:31:43.800 --> 00:31:45.726 So first if you don't downsample then 00:31:45.726 --> 00:31:48.020 you apply you define the convolution as 00:31:48.020 --> 00:31:49.553 the number of North channels to the 00:31:49.553 --> 00:31:50.510 number of out channels. 00:31:51.340 --> 00:31:54.000 And it's a three by three filter stride 00:31:54.000 --> 00:31:55.800 is 1 means that it operates over every 00:31:55.800 --> 00:31:56.320 position. 00:31:57.320 --> 00:31:59.270 And padding is one you like. 00:31:59.270 --> 00:32:01.040 Create some fake values around the 00:32:01.040 --> 00:32:03.020 outside of the feature map so that you 00:32:03.020 --> 00:32:05.905 can compute the filter on the border of 00:32:05.905 --> 00:32:06.460 the future map. 00:32:09.090 --> 00:32:09.820 The. 00:32:09.970 --> 00:32:10.630 00:32:12.070 --> 00:32:14.120 And then it's yeah, there's nothing. 00:32:14.120 --> 00:32:15.820 It's just saying that's what BN one is, 00:32:15.820 --> 00:32:18.599 a batch norm and then. 00:32:20.020 --> 00:32:21.810 Comp two is the same thing basically, 00:32:21.810 --> 00:32:23.360 except now the out channel, you're 00:32:23.360 --> 00:32:24.995 going from out channels to out 00:32:24.995 --> 00:32:25.280 channels. 00:32:25.280 --> 00:32:28.359 So this could go from like 64 to 128 00:32:28.360 --> 00:32:29.085 for example. 00:32:29.085 --> 00:32:31.480 And then this convolution you'd have 00:32:31.480 --> 00:32:34.850 like 128 filters that are operating on 00:32:34.850 --> 00:32:36.580 a 64 deep. 00:32:37.560 --> 00:32:38.180 Feature map. 00:32:38.820 --> 00:32:42.010 And then here you'd have 128 filters 00:32:42.010 --> 00:32:44.470 operating on a 128 deep feature map. 00:32:46.830 --> 00:32:49.250 Then if you do the downsampling, you 00:32:49.250 --> 00:32:51.170 instead of that first convolution. 00:32:51.170 --> 00:32:53.000 That first convolutional layer is what 00:32:53.000 --> 00:32:54.140 does the down sampling. 00:32:55.100 --> 00:32:56.690 And the way that it does it. 00:32:56.840 --> 00:32:57.430 00:32:58.220 --> 00:33:00.130 Is that it does. 00:33:00.130 --> 00:33:02.240 It has a stride of two, so instead of 00:33:02.240 --> 00:33:03.660 operating at every position, you 00:33:03.660 --> 00:33:05.518 operate on every other position. 00:33:05.518 --> 00:33:07.380 And then since you have an output at 00:33:07.380 --> 00:33:09.280 every other position, it means that 00:33:09.280 --> 00:33:11.250 you'll only have half as many outputs 00:33:11.250 --> 00:33:12.970 along each dimension, and so that will 00:33:12.970 --> 00:33:14.970 make the feature map smaller spatially. 00:33:20.700 --> 00:33:22.090 And so here's a whole. 00:33:22.090 --> 00:33:23.970 Here's the rest of the code for the 00:33:23.970 --> 00:33:25.060 Resnet architecture. 00:33:25.990 --> 00:33:28.225 So first the forward is pretty simple. 00:33:28.225 --> 00:33:31.690 It goes layer 01234. 00:33:31.690 --> 00:33:33.000 This is average pooling. 00:33:33.000 --> 00:33:35.085 So at some point you take all the 00:33:35.085 --> 00:33:36.856 features that are in some little map 00:33:36.856 --> 00:33:38.890 and you just string them all up into a 00:33:38.890 --> 00:33:39.930 big vector. 00:33:39.930 --> 00:33:41.880 Or sorry actually no I said it wrong. 00:33:41.880 --> 00:33:43.596 You take all the features that are into 00:33:43.596 --> 00:33:45.310 a map and then you take the average 00:33:45.310 --> 00:33:46.480 across all the cells. 00:33:46.480 --> 00:33:48.870 So if you had like a three by three map 00:33:48.870 --> 00:33:49.560 of features. 00:33:50.430 --> 00:33:52.220 Then for each feature value you would 00:33:52.220 --> 00:33:53.890 take the average of those nine 00:33:53.890 --> 00:33:56.120 elements, like the 9 spatial positions. 00:33:56.770 --> 00:34:00.460 And that's the average pooling. 00:34:01.720 --> 00:34:03.880 And then the flatten is where you then 00:34:03.880 --> 00:34:06.060 take that you just make it into a big 00:34:06.060 --> 00:34:07.010 long feature vector. 00:34:08.240 --> 00:34:10.315 And then you have your final linear 00:34:10.315 --> 00:34:11.890 layer, so the FC layer. 00:34:13.170 --> 00:34:14.480 And then if you look at the details, 00:34:14.480 --> 00:34:17.020 it's just basically each layer is 00:34:17.020 --> 00:34:18.000 simply. 00:34:18.000 --> 00:34:20.490 The first layer is special, you do a 00:34:20.490 --> 00:34:22.430 convolution with the bigger size 00:34:22.430 --> 00:34:22.800 filter. 00:34:24.180 --> 00:34:26.535 And you also do some Max pooling, so 00:34:26.535 --> 00:34:28.610 you kind of quickly get it into a 00:34:28.610 --> 00:34:30.560 deeper level of a deeper number of 00:34:30.560 --> 00:34:32.280 features at a smaller spatial 00:34:32.280 --> 00:34:32.800 dimension. 00:34:33.760 --> 00:34:35.170 And then in the subsequent layers, 00:34:35.170 --> 00:34:37.740 they're all very similar, you just you 00:34:37.740 --> 00:34:39.210 have two res blocks. 00:34:40.230 --> 00:34:41.780 Two rez blocks, then you have two res 00:34:41.780 --> 00:34:43.762 blocks where you're down sampling and 00:34:43.762 --> 00:34:45.420 increasing and increasing the depth, 00:34:45.420 --> 00:34:47.310 increasing the number of features. 00:34:47.310 --> 00:34:49.126 Downsample increase the number of 00:34:49.126 --> 00:34:50.428 features, down sample increase the 00:34:50.428 --> 00:34:51.160 number of features. 00:34:51.890 --> 00:34:54.250 Average in your final linear layer. 00:34:59.540 --> 00:35:01.840 And then this is some examples of 00:35:01.840 --> 00:35:03.600 different different depths. 00:35:04.780 --> 00:35:08.180 So mainly they just differ in how many 00:35:08.180 --> 00:35:10.650 res blocks you apply and for the larger 00:35:10.650 --> 00:35:12.230 ones, they're doing the bottleneck 00:35:12.230 --> 00:35:15.239 Resnet module instead of like the 00:35:15.240 --> 00:35:17.520 simpler RESNET module that we showed 00:35:17.520 --> 00:35:18.080 in. 00:35:18.080 --> 00:35:19.690 So this is the one that I was showing 00:35:19.690 --> 00:35:20.680 on the earlier slide. 00:35:21.440 --> 00:35:22.820 And this is the code. 00:35:22.820 --> 00:35:24.270 I was showing the code for this one 00:35:24.270 --> 00:35:25.570 which is just a little bit simpler. 00:35:27.550 --> 00:35:28.975 So you start with a. 00:35:28.975 --> 00:35:32.145 So the input to this for Imagenet is a 00:35:32.145 --> 00:35:34.180 224 by 224 image. 00:35:34.180 --> 00:35:35.960 That's RGB. 00:35:37.050 --> 00:35:39.550 And then since the first comp layer is 00:35:39.550 --> 00:35:41.620 doing a stride of two, it ends up being 00:35:41.620 --> 00:35:45.600 a 112 by 112 like image that then has 00:35:45.600 --> 00:35:47.860 64 features per position. 00:35:49.240 --> 00:35:51.890 And then you do another Max pooling 00:35:51.890 --> 00:35:53.680 which is taking the Max value out of 00:35:53.680 --> 00:35:55.490 each two by two chunk of the image. 00:35:55.490 --> 00:35:58.475 So that further reduces the size to 56 00:35:58.475 --> 00:35:59.260 by 56. 00:36:00.600 --> 00:36:05.130 Then you these resnet blocks to it, so 00:36:05.130 --> 00:36:08.999 then the output is still 56 is still 56 00:36:09.000 --> 00:36:09.840 by 56. 00:36:10.620 --> 00:36:12.260 And then you apply Resnet blocks that 00:36:12.260 --> 00:36:14.860 will downsample it by a factor of two. 00:36:14.860 --> 00:36:18.079 So now you've got a 22028 by 28 by. 00:36:18.080 --> 00:36:20.210 If you're doing like Resnet 34, it'd be 00:36:20.210 --> 00:36:21.520 128 dimensional features. 00:36:22.450 --> 00:36:24.315 And then again you downsample, produce 00:36:24.315 --> 00:36:26.965 more features, down sample produce more 00:36:26.965 --> 00:36:27.360 features. 00:36:28.010 --> 00:36:32.570 Average pool and then you finally like 00:36:32.570 --> 00:36:34.740 turn this into like a 512 dimensional 00:36:34.740 --> 00:36:35.440 feature vector. 00:36:35.440 --> 00:36:38.070 If you're doing Resnet 34 or if you 00:36:38.070 --> 00:36:41.021 were doing Resnet 50 to 152, you'd have 00:36:41.021 --> 00:36:42.879 a 2048 dimensional feature vector. 00:36:45.450 --> 00:36:45.950 00:36:50.330 --> 00:36:52.575 So this is multiple blocks. 00:36:52.575 --> 00:36:55.250 So if you see here like each layer for 00:36:55.250 --> 00:36:57.320 Resnet 18 add 2 rez blocks in a row. 00:36:58.330 --> 00:37:01.410 So this is for 18 we have like times 2. 00:37:02.110 --> 00:37:03.940 And if you go deeper, then you just 00:37:03.940 --> 00:37:06.090 have more rest blocks in a row at the 00:37:06.090 --> 00:37:06.890 same size. 00:37:09.000 --> 00:37:11.360 So each of these layers is transforming 00:37:11.360 --> 00:37:12.720 the feature, is trying to extract 00:37:12.720 --> 00:37:15.640 useful information and also like kind 00:37:15.640 --> 00:37:17.700 of deepening, deepening the features 00:37:17.700 --> 00:37:19.190 and looking broader in the image. 00:37:20.940 --> 00:37:22.570 And then this is showing the number, 00:37:22.570 --> 00:37:25.195 the amount of computation roughly that 00:37:25.195 --> 00:37:26.870 is used for each of these. 00:37:26.870 --> 00:37:28.480 And one thing to note is that when you 00:37:28.480 --> 00:37:31.250 go from 34 to 50, that's when they 00:37:31.250 --> 00:37:32.960 start using the bottleneck layer, so 00:37:32.960 --> 00:37:34.290 you there's almost no change in 00:37:34.290 --> 00:37:36.830 computation, even though the 50 is much 00:37:36.830 --> 00:37:39.410 deeper and as many more has many more 00:37:39.410 --> 00:37:39.800 layers. 00:37:41.610 --> 00:37:45.330 And then finally remember that the like 00:37:45.330 --> 00:37:47.810 breakthrough result from Alex NET was 00:37:47.810 --> 00:37:50.800 15% error roughly for top five 00:37:50.800 --> 00:37:51.800 prediction. 00:37:52.750 --> 00:37:56.500 And Resnet 152 gets 4.5% error for top 00:37:56.500 --> 00:37:57.210 five prediction. 00:37:57.980 --> 00:38:00.762 So that's a factor that's more than a 00:38:00.762 --> 00:38:02.420 factor of 3 reduction of error, which 00:38:02.420 --> 00:38:03.410 is really huge. 00:38:06.830 --> 00:38:09.980 And nothing gets like that much 00:38:09.980 --> 00:38:11.120 remarkably better. 00:38:11.120 --> 00:38:11.760 There's no. 00:38:11.760 --> 00:38:13.740 I don't remember what the best is, but 00:38:13.740 --> 00:38:15.070 it's not like that much better than. 00:38:20.020 --> 00:38:20.350 All right. 00:38:21.090 --> 00:38:23.190 So that's resnet. 00:38:23.190 --> 00:38:23.930 One more. 00:38:23.930 --> 00:38:25.940 This is sort of like a sidebar note I 00:38:25.940 --> 00:38:28.220 applies to all the vision methods as 00:38:28.220 --> 00:38:28.480 well. 00:38:29.160 --> 00:38:30.810 A really common trick in computer 00:38:30.810 --> 00:38:32.600 vision is that you do what's called 00:38:32.600 --> 00:38:33.660 data augmentation. 00:38:34.630 --> 00:38:35.650 Which is that like. 00:38:36.310 --> 00:38:39.110 Each image show the example here. 00:38:39.110 --> 00:38:40.300 So each image. 00:38:41.800 --> 00:38:44.320 You can like change the image in small 00:38:44.320 --> 00:38:46.330 ways and it will create different 00:38:46.330 --> 00:38:47.980 features, but we would say like you 00:38:47.980 --> 00:38:50.130 should interpret all of these images to 00:38:50.130 --> 00:38:50.780 be the same. 00:38:51.510 --> 00:38:53.530 So you might have like a photo like 00:38:53.530 --> 00:38:55.450 this and you can like modify the 00:38:55.450 --> 00:38:57.379 coloring or you can. 00:38:58.740 --> 00:39:01.340 You can apply like some specialized 00:39:01.340 --> 00:39:03.300 filters to it or you can crop it or 00:39:03.300 --> 00:39:05.860 shift it or rotate it and we would 00:39:05.860 --> 00:39:07.684 typically say like these all have the 00:39:07.684 --> 00:39:08.980 same content, all these different 00:39:08.980 --> 00:39:10.180 images have the same content. 00:39:10.860 --> 00:39:12.550 But they're gonna produce slightly 00:39:12.550 --> 00:39:14.040 different features because something 00:39:14.040 --> 00:39:14.640 was done to them. 00:39:15.620 --> 00:39:17.330 And since you're cycling through the 00:39:17.330 --> 00:39:17.720 data. 00:39:18.370 --> 00:39:23.070 Many times, sometimes 100 or 300 times. 00:39:23.070 --> 00:39:24.430 Then it kind of makes sense to create 00:39:24.430 --> 00:39:26.140 little variations of the data rather 00:39:26.140 --> 00:39:28.340 than processing the exact same data 00:39:28.340 --> 00:39:29.550 every time you pass through it. 00:39:30.670 --> 00:39:34.485 And so the idea of data augmentation is 00:39:34.485 --> 00:39:36.495 that you create more variety to your 00:39:36.495 --> 00:39:38.370 data and that kind of like creates 00:39:38.370 --> 00:39:40.600 virtual training examples that can 00:39:40.600 --> 00:39:43.570 further improve the robustness of the 00:39:43.570 --> 00:39:43.940 model. 00:39:45.030 --> 00:39:47.700 So this idea goes back to Palmer low in 00:39:47.700 --> 00:39:50.110 1995, who used neural networks to drive 00:39:50.110 --> 00:39:50.590 a car. 00:39:52.130 --> 00:39:54.740 But it's it was like picked up again 00:39:54.740 --> 00:39:57.460 more broadly when deep networks became 00:39:57.460 --> 00:39:57.860 popular. 00:40:00.730 --> 00:40:01.050 Yeah. 00:40:02.660 --> 00:40:04.286 And then to do data augmentation, you 00:40:04.286 --> 00:40:06.890 do it like this, where you define like 00:40:06.890 --> 00:40:07.330 transforms. 00:40:07.330 --> 00:40:09.313 If you're doing it in π torch, you 00:40:09.313 --> 00:40:11.210 define like the set of transforms that 00:40:11.210 --> 00:40:13.547 apply, and there'll be some like 00:40:13.547 --> 00:40:15.699 randomly mirror, randomly apply some 00:40:15.700 --> 00:40:18.380 rotation within this range, randomly 00:40:18.380 --> 00:40:22.380 resize within some range, randomly 00:40:22.380 --> 00:40:22.740 crop. 00:40:23.750 --> 00:40:25.940 And then? 00:40:27.150 --> 00:40:29.210 And so then like every time the data is 00:40:29.210 --> 00:40:31.080 loaded, then the data loader will like 00:40:31.080 --> 00:40:33.410 apply these transformations so that 00:40:33.410 --> 00:40:35.580 your data gets like modified in these 00:40:35.580 --> 00:40:36.270 various ways. 00:40:37.930 --> 00:40:39.600 That you apply the transform and then 00:40:39.600 --> 00:40:41.320 you input the transform into the data 00:40:41.320 --> 00:40:41.670 loader. 00:40:48.210 --> 00:40:48.830 So. 00:40:50.610 --> 00:40:53.920 So far I've talked about one data set, 00:40:53.920 --> 00:40:54.500 Imagenet. 00:40:55.120 --> 00:40:57.630 And one and some different 00:40:57.630 --> 00:40:58.230 architectures. 00:40:59.570 --> 00:41:03.670 But nobody, Imagenet, is not itself 00:41:03.670 --> 00:41:05.550 like a very practical application, 00:41:05.550 --> 00:41:05.780 right? 00:41:05.780 --> 00:41:08.450 Like nobody wants to classify images 00:41:08.450 --> 00:41:09.670 into those thousand categories. 00:41:11.300 --> 00:41:13.920 And the even after the successive 00:41:13.920 --> 00:41:15.530 Imagenet, it wasn't clear like what 00:41:15.530 --> 00:41:17.110 will be the impact on computer vision, 00:41:17.110 --> 00:41:18.960 because most of our data sets are not 00:41:18.960 --> 00:41:20.355 nearly that big. 00:41:20.355 --> 00:41:22.330 It took a lot of work to create 00:41:22.330 --> 00:41:22.770 Imagenet. 00:41:22.770 --> 00:41:24.420 And if you're just trying to. 00:41:25.450 --> 00:41:27.200 Do some kind of application for your 00:41:27.200 --> 00:41:29.330 company or for personal project or 00:41:29.330 --> 00:41:30.000 whatever. 00:41:30.000 --> 00:41:33.330 Chances are it's like very expensive to 00:41:33.330 --> 00:41:34.670 get that amount of data and you might 00:41:34.670 --> 00:41:36.880 not have that many images available so. 00:41:37.650 --> 00:41:41.168 Is this useful for smaller, for smaller 00:41:41.168 --> 00:41:42.170 data sets? 00:41:42.170 --> 00:41:43.830 Or problems were not so much data is 00:41:43.830 --> 00:41:44.350 available? 00:41:45.200 --> 00:41:47.580 And so that brings us to the problem of 00:41:47.580 --> 00:41:49.410 like how we can take a model that's 00:41:49.410 --> 00:41:52.200 trained for one data set, Imagenet, and 00:41:52.200 --> 00:41:54.759 then apply it to some other data set. 00:41:55.670 --> 00:41:57.750 You can think about these when you're 00:41:57.750 --> 00:42:01.000 training a deep network to do Imagenet. 00:42:01.000 --> 00:42:03.030 It's not only learning to classify 00:42:03.030 --> 00:42:05.380 images into these Imagenet labels, but 00:42:05.380 --> 00:42:07.770 it's also learning a representation of 00:42:07.770 --> 00:42:09.794 images, and it's that representation 00:42:09.794 --> 00:42:11.300 that can be reused. 00:42:12.250 --> 00:42:13.560 And the reason that the Imagenet 00:42:13.560 --> 00:42:16.336 representation is like pretty effective 00:42:16.336 --> 00:42:18.676 is that there's so many different 00:42:18.676 --> 00:42:21.374 classes and so many different, so many 00:42:21.374 --> 00:42:22.320 different images. 00:42:22.320 --> 00:42:24.420 And so a representation that can 00:42:24.420 --> 00:42:26.410 distinguish between these thousand 00:42:26.410 --> 00:42:29.720 classes also probably encodes like most 00:42:29.720 --> 00:42:31.060 of the information that you would need 00:42:31.060 --> 00:42:32.460 to do many other vision tests. 00:42:37.550 --> 00:42:40.540 So we start with this Imagenet, what 00:42:40.540 --> 00:42:42.300 you would call a pre trained model. 00:42:42.300 --> 00:42:43.840 So it was like trained on Imagenet. 00:42:44.600 --> 00:42:46.680 And we can think of it as having two 00:42:46.680 --> 00:42:47.270 components. 00:42:47.270 --> 00:42:49.660 There's the encoder that's producing a 00:42:49.660 --> 00:42:51.780 good representation of the image, and 00:42:51.780 --> 00:42:54.720 then a decoder linear layer that is 00:42:54.720 --> 00:42:56.875 mapping from that encoded image 00:42:56.875 --> 00:42:59.700 representation into some class logics. 00:43:00.400 --> 00:43:01.340 Or probabilities? 00:43:05.320 --> 00:43:09.555 SO11 common solution to this is what's 00:43:09.555 --> 00:43:11.630 called sometimes called a linear probe 00:43:11.630 --> 00:43:13.410 now or feature extraction. 00:43:14.410 --> 00:43:17.880 So basically you just you essentially 00:43:17.880 --> 00:43:18.700 just compute. 00:43:18.700 --> 00:43:20.340 You don't change any of the weights in 00:43:20.340 --> 00:43:22.090 all these like convolutional layers. 00:43:23.230 --> 00:43:25.250 You throw out the decoder, so your 00:43:25.250 --> 00:43:26.520 final linear prediction. 00:43:26.520 --> 00:43:27.870 You get rid of it because you want to 00:43:27.870 --> 00:43:29.420 classify something different this time. 00:43:30.170 --> 00:43:32.030 Any replace that final linear 00:43:32.030 --> 00:43:35.540 prediction with a new linear layer 00:43:35.540 --> 00:43:36.900 that's going to predict the classes 00:43:36.900 --> 00:43:38.080 that you actually care about. 00:43:39.040 --> 00:43:41.090 And then, without changing the encoder 00:43:41.090 --> 00:43:45.560 at all, you then extract the same kind 00:43:45.560 --> 00:43:46.980 of features from your new training 00:43:46.980 --> 00:43:49.020 examples, and now you predict the new 00:43:49.020 --> 00:43:52.030 labels that you care about and tune 00:43:52.030 --> 00:43:54.525 your linear model, your final decoder, 00:43:54.525 --> 00:43:56.770 to make those predictions well. 00:43:59.950 --> 00:44:02.550 So there's like two ways to do this. 00:44:02.760 --> 00:44:03.310 00:44:04.320 --> 00:44:08.460 One way is the feature method where you 00:44:08.460 --> 00:44:10.690 basically you just extract features 00:44:10.690 --> 00:44:12.120 using the pre trained model. 00:44:13.030 --> 00:44:14.952 And you save those features and then 00:44:14.952 --> 00:44:17.960 you can just train an SVM or a linear 00:44:17.960 --> 00:44:20.470 logistic regression classifier. 00:44:20.470 --> 00:44:24.140 So this was the way that this is 00:44:24.140 --> 00:44:27.330 actually a very fast way and what the 00:44:27.330 --> 00:44:28.953 very earliest papers would extract 00:44:28.953 --> 00:44:30.660 features this way and then apply an 00:44:30.660 --> 00:44:30.970 SVM. 00:44:33.240 --> 00:44:36.010 So you load pre trained model. 00:44:36.010 --> 00:44:36.970 There's many. 00:44:36.970 --> 00:44:38.946 If you go to this link there's tons of 00:44:38.946 --> 00:44:40.130 pre trained models available. 00:44:41.450 --> 00:44:43.466 You remove the final prediction layer, 00:44:43.466 --> 00:44:45.120 the final linear classifier. 00:44:46.130 --> 00:44:48.360 You apply the model to each of your new 00:44:48.360 --> 00:44:50.310 training images to get their features, 00:44:50.310 --> 00:44:51.340 and then you save them. 00:44:52.150 --> 00:44:54.345 And so you have just a linear you have 00:44:54.345 --> 00:44:56.335 like a data set where you have X your 00:44:56.335 --> 00:44:57.570 features from the deep network. 00:44:57.570 --> 00:44:59.350 It will be for example like 512 00:44:59.350 --> 00:45:00.990 dimensional features for each image. 00:45:01.940 --> 00:45:05.460 And your labels that you've annotated 00:45:05.460 --> 00:45:07.790 for your new classification problem. 00:45:08.490 --> 00:45:09.925 And then you just train a new linear 00:45:09.925 --> 00:45:11.410 model and you can use whatever you 00:45:11.410 --> 00:45:11.560 want. 00:45:11.560 --> 00:45:12.813 It doesn't even have to be a linear 00:45:12.813 --> 00:45:14.340 model, but usually that's what people 00:45:14.340 --> 00:45:14.680 would do. 00:45:16.690 --> 00:45:17.960 So here's the code for that. 00:45:17.960 --> 00:45:19.770 It's like pretty trivial. 00:45:21.060 --> 00:45:21.730 Very short. 00:45:22.380 --> 00:45:25.210 So you get let's say you want Alex net, 00:45:25.210 --> 00:45:29.900 you just like, import these things in 00:45:29.900 --> 00:45:31.080 your notebook or whatever. 00:45:31.880 --> 00:45:34.022 You get the Alex net model, set it to 00:45:34.022 --> 00:45:34.660 be pre trained. 00:45:34.660 --> 00:45:35.670 So this will be pre trained on 00:45:35.670 --> 00:45:36.090 Imagenet. 00:45:36.750 --> 00:45:38.920 This pre trained equals true will work, 00:45:38.920 --> 00:45:40.290 but it's deprecated. 00:45:40.290 --> 00:45:41.780 It will get you the Imagenet model but 00:45:41.780 --> 00:45:43.090 there's actually like you can get 00:45:43.090 --> 00:45:44.410 models that are pre trained on other 00:45:44.410 --> 00:45:45.090 things as well. 00:45:46.820 --> 00:45:49.770 And then this is just a very compact 00:45:49.770 --> 00:45:52.100 way of chopping off the last layer, so 00:45:52.100 --> 00:45:54.410 it's like keeping all the layers up to 00:45:54.410 --> 00:45:55.180 the last one. 00:45:56.580 --> 00:45:58.490 And then you just. 00:45:58.490 --> 00:46:00.800 So this is like doing steps one and 00:46:00.800 --> 00:46:01.130 two. 00:46:01.760 --> 00:46:03.280 And then you would just loop through 00:46:03.280 --> 00:46:06.805 your new images, use this, apply this 00:46:06.805 --> 00:46:09.682 new model to your images to get the 00:46:09.682 --> 00:46:11.360 features and then save those features. 00:46:13.640 --> 00:46:17.350 The other method that you can do is 00:46:17.350 --> 00:46:19.520 that you just like freeze your encoder 00:46:19.520 --> 00:46:22.260 so that term freeze or frozen weights. 00:46:23.460 --> 00:46:25.465 Is means that you don't allow the 00:46:25.465 --> 00:46:26.500 weights to change. 00:46:26.500 --> 00:46:29.095 So you process examples using those 00:46:29.095 --> 00:46:30.960 weights, but you don't update the 00:46:30.960 --> 00:46:31.840 weights during training. 00:46:32.960 --> 00:46:34.830 So again you load pre trained model. 00:46:35.710 --> 00:46:38.390 You set the network to not update the 00:46:38.390 --> 00:46:39.280 encoder weights. 00:46:39.280 --> 00:46:41.360 You replace the last layer just like 00:46:41.360 --> 00:46:42.980 before with your new linear layer. 00:46:43.630 --> 00:46:45.220 And then you train the network with the 00:46:45.220 --> 00:46:45.810 new data set. 00:46:46.990 --> 00:46:49.284 And this is a little bit slower or a 00:46:49.284 --> 00:46:50.810 bit slower than the method on the left, 00:46:50.810 --> 00:46:52.280 because every time you process a 00:46:52.280 --> 00:46:53.500 training sample you have to run it 00:46:53.500 --> 00:46:54.320 through the whole network. 00:46:55.000 --> 00:46:58.130 But it but then the advantages are that 00:46:58.130 --> 00:46:59.570 you don't have to store any features. 00:47:00.250 --> 00:47:01.720 And you can also apply data 00:47:01.720 --> 00:47:03.350 augmentation, so you can create like 00:47:03.350 --> 00:47:05.910 there's random variations each time you 00:47:05.910 --> 00:47:08.500 process the training data and pass it 00:47:08.500 --> 00:47:10.470 and then like process it through the 00:47:10.470 --> 00:47:10.780 network. 00:47:12.040 --> 00:47:14.980 So this code is also pretty simple. 00:47:14.980 --> 00:47:15.560 You do. 00:47:16.920 --> 00:47:18.610 For each of your model parameters, 00:47:18.610 --> 00:47:20.650 first you set requires grad equals 00:47:20.650 --> 00:47:22.420 false, which means that it's not going 00:47:22.420 --> 00:47:23.640 to update them or compute the 00:47:23.640 --> 00:47:24.150 gradients. 00:47:25.090 --> 00:47:28.885 And then you just set the last layer 00:47:28.885 --> 00:47:33.020 the model that FC is a new layer and an 00:47:33.020 --> 00:47:33.630 linear. 00:47:33.630 --> 00:47:34.630 So this is if you're. 00:47:35.780 --> 00:47:38.050 Doing a Resnet 34 for example, where 00:47:38.050 --> 00:47:40.010 the output is 512 dimensional. 00:47:40.600 --> 00:47:41.970 And this would be mapping into eight 00:47:41.970 --> 00:47:43.340 classes in this example. 00:47:45.230 --> 00:47:47.615 And then since when you add a new layer 00:47:47.615 --> 00:47:49.810 it by default gradients are on South 00:47:49.810 --> 00:47:51.410 the gradients, so then when you train 00:47:51.410 --> 00:47:53.030 this network it won't change the 00:47:53.030 --> 00:47:54.670 encoder at all, it will only change 00:47:54.670 --> 00:47:56.310 your final classification layer. 00:47:57.240 --> 00:47:59.500 And this model, CUDA is just saying 00:47:59.500 --> 00:48:01.320 that you're going to be putting it into 00:48:01.320 --> 00:48:01.990 the GPU. 00:48:05.190 --> 00:48:05.510 Question. 00:48:17.580 --> 00:48:20.550 So you're so the question is, what does 00:48:20.550 --> 00:48:21.680 it mean to train if you're not updating 00:48:21.680 --> 00:48:22.100 the weights? 00:48:22.100 --> 00:48:23.610 Well, you're just updating these 00:48:23.610 --> 00:48:24.870 weights, the decoder weights. 00:48:25.530 --> 00:48:27.340 So you're training the last linear 00:48:27.340 --> 00:48:29.220 layer, but these are not changing at 00:48:29.220 --> 00:48:31.670 all, so it's producing the same. 00:48:31.670 --> 00:48:33.280 The features that it produces for a 00:48:33.280 --> 00:48:35.236 given image doesn't change during the 00:48:35.236 --> 00:48:37.300 training, but then the classification 00:48:37.300 --> 00:48:40.020 from those features into your class 00:48:40.020 --> 00:48:41.130 scores does change. 00:48:47.240 --> 00:48:49.330 Alright, so the next the next solution 00:48:49.330 --> 00:48:50.830 is called fine tuning. 00:48:50.830 --> 00:48:52.770 That is also like a term that you will 00:48:52.770 --> 00:48:54.470 run into all the time without any 00:48:54.470 --> 00:48:55.160 explanation. 00:48:57.030 --> 00:48:59.880 So this is actually really unintuitive. 00:49:00.790 --> 00:49:03.700 Or it may be intuitive, but it's kind 00:49:03.700 --> 00:49:04.260 of not. 00:49:04.260 --> 00:49:05.860 You wouldn't necessarily think this 00:49:05.860 --> 00:49:07.730 would work if you didn't know it works. 00:49:09.020 --> 00:49:10.880 So the idea of fine tuning is actually 00:49:10.880 --> 00:49:12.480 just you allow all the weights to 00:49:12.480 --> 00:49:13.090 change. 00:49:13.970 --> 00:49:15.610 But you just set a much smaller 00:49:15.610 --> 00:49:18.050 learning rate, so it can't change as 00:49:18.050 --> 00:49:21.070 easily or it won't change as much, so 00:49:21.070 --> 00:49:21.970 it's a little. 00:49:21.970 --> 00:49:24.200 What's weird about it is that usually 00:49:24.200 --> 00:49:26.215 when you have an optimization problem, 00:49:26.215 --> 00:49:27.880 you want to try to like. 00:49:28.600 --> 00:49:29.950 Solve that. 00:49:31.170 --> 00:49:32.320 You want to like solve that 00:49:32.320 --> 00:49:33.690 optimization problem as well as 00:49:33.690 --> 00:49:35.110 possible to get the best score in your 00:49:35.110 --> 00:49:35.620 objective. 00:49:36.390 --> 00:49:38.980 And in this case, you're actually 00:49:38.980 --> 00:49:40.492 trying to hit a local minima. 00:49:40.492 --> 00:49:42.780 You're trying to get into a suboptimal 00:49:42.780 --> 00:49:44.200 solution according to your objective 00:49:44.200 --> 00:49:46.790 function by starting with what you 00:49:46.790 --> 00:49:49.070 think a priori is a good solution and 00:49:49.070 --> 00:49:50.920 allowing it to not drift too far from 00:49:50.920 --> 00:49:51.600 that solution. 00:49:52.960 --> 00:49:55.410 So there's for example, like, let's 00:49:55.410 --> 00:49:56.965 suppose that you had this thing trained 00:49:56.965 --> 00:49:57.890 on Imagenet. 00:49:57.890 --> 00:50:00.356 It's trained on like millions of images 00:50:00.356 --> 00:50:01.616 on thousands of classes. 00:50:01.616 --> 00:50:03.010 So you're pretty confident that it 00:50:03.010 --> 00:50:04.900 learned a really good representation. 00:50:04.900 --> 00:50:06.665 But you have some new data set where 00:50:06.665 --> 00:50:08.916 you have 10 different classes and you 00:50:08.916 --> 00:50:11.055 have 100 images for each of those ten 00:50:11.055 --> 00:50:11.680 classes. 00:50:11.680 --> 00:50:13.950 Now that's like not enough data to 00:50:13.950 --> 00:50:15.866 really learn your encoder. 00:50:15.866 --> 00:50:18.630 It's not enough data to learn this big 00:50:18.630 --> 00:50:19.170 like. 00:50:20.130 --> 00:50:21.860 Million parameter network. 00:50:23.410 --> 00:50:25.200 On the other hand, maybe Imagenet is 00:50:25.200 --> 00:50:27.130 not the perfect representation for your 00:50:27.130 --> 00:50:29.870 new tasks, so you want to allow the 00:50:29.870 --> 00:50:31.570 training to just kind of tweak your 00:50:31.570 --> 00:50:34.550 encoding, to tweak your deep network 00:50:34.550 --> 00:50:37.520 and to learn a new linear layer, but 00:50:37.520 --> 00:50:40.459 not to totally redo the network. 00:50:41.540 --> 00:50:44.470 So it's like a really hacky solution, 00:50:44.470 --> 00:50:46.210 but it works really in practice that 00:50:46.210 --> 00:50:47.835 you just set a lower learning rate. 00:50:47.835 --> 00:50:50.190 So you say you use like a 10X smaller 00:50:50.190 --> 00:50:51.250 learning rate than normal. 00:50:51.950 --> 00:50:53.790 And you train it just you normally 00:50:53.790 --> 00:50:54.080 would. 00:50:54.080 --> 00:50:55.740 So you download the model, you start 00:50:55.740 --> 00:50:57.390 with that, use that as your starting 00:50:57.390 --> 00:50:59.420 point, and then you train it with a low 00:50:59.420 --> 00:51:01.330 learning rate and that's it. 00:51:03.770 --> 00:51:05.770 So how in more detail? 00:51:06.510 --> 00:51:08.630 Load the train model just like before I 00:51:08.630 --> 00:51:09.890 replace the last layer. 00:51:09.890 --> 00:51:11.750 Set a lower learning rate so it could 00:51:11.750 --> 00:51:13.710 be like east to the -, 4 for example, 00:51:13.710 --> 00:51:15.690 instead of east to the -, 3 or E to the 00:51:15.690 --> 00:51:20.010 -, 2 meaning like .00010001. 00:51:20.930 --> 00:51:21.420 00:51:22.080 --> 00:51:23.939 The learning rate typically is like 10 00:51:23.940 --> 00:51:25.520 times smaller than what you would use 00:51:25.520 --> 00:51:27.220 if you were training something from 00:51:27.220 --> 00:51:29.000 scratch from random initialization. 00:51:30.090 --> 00:51:32.190 One trick that can help is that 00:51:32.190 --> 00:51:34.230 sometimes you would want to do like the 00:51:34.230 --> 00:51:35.840 freezing method to train your last 00:51:35.840 --> 00:51:38.510 layer classifier first and then you and 00:51:38.510 --> 00:51:40.090 then you start tuning the whole 00:51:40.090 --> 00:51:40.510 network. 00:51:40.510 --> 00:51:42.550 And the reason for that is that when 00:51:42.550 --> 00:51:45.160 you first like add the last layer in. 00:51:45.990 --> 00:51:48.070 For your new task, it's random weights, 00:51:48.070 --> 00:51:50.310 so it's a really bad classifier. 00:51:50.970 --> 00:51:52.950 So it's going to be sending all kinds 00:51:52.950 --> 00:51:54.790 of gradients back into the network 00:51:54.790 --> 00:51:56.950 based on its own terrible 00:51:56.950 --> 00:51:58.310 classification ability. 00:51:59.230 --> 00:52:00.970 And that will start to like mess up 00:52:00.970 --> 00:52:02.530 your really nice encoder. 00:52:03.280 --> 00:52:05.870 And so it can be better to 1st train 00:52:05.870 --> 00:52:08.740 the last layer and then like allow the 00:52:08.740 --> 00:52:11.480 encoder to start training so that it's 00:52:11.480 --> 00:52:14.650 getting more meaningful weight update 00:52:14.650 --> 00:52:15.830 signals from the classifier. 00:52:17.960 --> 00:52:19.833 The other the other trick you can do is 00:52:19.833 --> 00:52:21.500 you can set a different learning rate 00:52:21.500 --> 00:52:23.050 for the earlier layers than you do for 00:52:23.050 --> 00:52:25.300 the later layers or the final 00:52:25.300 --> 00:52:29.790 classifier, with the justification that 00:52:29.790 --> 00:52:31.240 your last layer is something that you 00:52:31.240 --> 00:52:33.272 need to train from scratch, so it needs 00:52:33.272 --> 00:52:35.356 to change a lot, but the earlier layers 00:52:35.356 --> 00:52:36.860 you don't want to change too much. 00:52:39.560 --> 00:52:42.490 Wagg created this notebook which shows 00:52:42.490 --> 00:52:44.160 like how you can customize the learning 00:52:44.160 --> 00:52:46.490 rate per layer, how you can initialize 00:52:46.490 --> 00:52:49.110 weights, freeze different parts of the 00:52:49.110 --> 00:52:49.880 network. 00:52:49.880 --> 00:52:52.140 So I'm not going to go through it in 00:52:52.140 --> 00:52:54.050 class, but it's a good thing to check 00:52:54.050 --> 00:52:55.980 out if you're interested in those 00:52:55.980 --> 00:52:56.490 details. 00:52:58.000 --> 00:53:01.080 So this code is like this is the fine 00:53:01.080 --> 00:53:01.920 tuning code. 00:53:01.920 --> 00:53:03.600 I'm well, I'm missing the training but 00:53:03.600 --> 00:53:05.120 the training is the same as it would be 00:53:05.120 --> 00:53:06.190 for training from scratch. 00:53:07.030 --> 00:53:09.300 You set the number of target classes, 00:53:09.300 --> 00:53:10.620 you load a model. 00:53:12.540 --> 00:53:14.410 And then you just replace the last 00:53:14.410 --> 00:53:16.820 layer with your new layer that has. 00:53:16.820 --> 00:53:18.170 This should have the same number of 00:53:18.170 --> 00:53:20.330 features that this model produces. 00:53:21.060 --> 00:53:22.600 And output into the number of target 00:53:22.600 --> 00:53:23.060 classes. 00:53:24.200 --> 00:53:28.725 So it's like it's just so easy like to 00:53:28.725 --> 00:53:32.000 train to get to train a new vision 00:53:32.000 --> 00:53:33.980 classifier for your task once you have 00:53:33.980 --> 00:53:36.105 the data, once you have the images and 00:53:36.105 --> 00:53:38.957 the labels, it's like boilerplate code 00:53:38.957 --> 00:53:41.494 to like train anything for that and it 00:53:41.494 --> 00:53:43.860 will work pretty well and it will make 00:53:43.860 --> 00:53:46.630 use of this massive Imagenet data set 00:53:46.630 --> 00:53:48.140 that has like trained a really good 00:53:48.140 --> 00:53:48.800 encoder for you. 00:53:50.080 --> 00:53:50.440 Question. 00:53:58.520 --> 00:54:01.120 The last layer because it's totally New 00:54:01.120 --> 00:54:02.690 South the. 00:54:03.730 --> 00:54:05.770 So here the last layer is you're new 00:54:05.770 --> 00:54:06.160 decoder. 00:54:06.160 --> 00:54:08.692 It's your linear classifier for your 00:54:08.692 --> 00:54:09.718 new task. 00:54:09.718 --> 00:54:14.340 So you have to train that layer in 00:54:14.340 --> 00:54:15.660 order to do your new classification 00:54:15.660 --> 00:54:15.990 task. 00:54:16.720 --> 00:54:19.860 While this was already initialized by 00:54:19.860 --> 00:54:20.550 Imagenet. 00:54:21.390 --> 00:54:23.340 It's not really that common to set 00:54:23.340 --> 00:54:24.810 different learning rates for different 00:54:24.810 --> 00:54:26.020 parts of the encoder. 00:54:26.020 --> 00:54:28.050 So you could say like maybe the later 00:54:28.050 --> 00:54:30.300 layers are like because they represent 00:54:30.300 --> 00:54:31.503 higher level features. 00:54:31.503 --> 00:54:34.630 Are like should change more where the 00:54:34.630 --> 00:54:36.247 earlier layers are representing simpler 00:54:36.247 --> 00:54:37.790 features that are going to be more 00:54:37.790 --> 00:54:38.640 generally useful. 00:54:39.650 --> 00:54:41.650 But it's not really common to do that. 00:54:41.650 --> 00:54:43.996 There's some justification, but it's 00:54:43.996 --> 00:54:44.990 not common practice. 00:54:53.760 --> 00:54:56.820 They're like higher level features. 00:54:56.820 --> 00:54:59.660 I'll show some I can share some 00:54:59.660 --> 00:55:01.320 examples of, like what they represent. 00:55:02.460 --> 00:55:03.960 So this is just showing. 00:55:04.060 --> 00:55:04.650 00:55:05.930 --> 00:55:10.005 The If you look at the performance of 00:55:10.005 --> 00:55:12.630 these different transfer methods as you 00:55:12.630 --> 00:55:14.498 vary the number of training samples. 00:55:14.498 --> 00:55:16.210 So here it's shown where it's showing 00:55:16.210 --> 00:55:18.190 the axis is actually 1 divided by the 00:55:18.190 --> 00:55:20.000 number of training samples per class or 00:55:20.000 --> 00:55:21.850 square root of that, so that these end 00:55:21.850 --> 00:55:24.310 up being like roughly linear curves. 00:55:25.510 --> 00:55:27.650 But for example, this means that 00:55:27.650 --> 00:55:29.731 there's 400 training samples per Class, 00:55:29.731 --> 00:55:31.686 100 training examples per class, 45 00:55:31.686 --> 00:55:32.989 training examples per class. 00:55:33.700 --> 00:55:36.160 The green is if you train from scratch 00:55:36.160 --> 00:55:37.930 so and this is the error. 00:55:37.930 --> 00:55:40.060 So if you have very few examples, then 00:55:40.060 --> 00:55:41.930 training from scratch performs really 00:55:41.930 --> 00:55:43.610 badly because you don't have enough 00:55:43.610 --> 00:55:44.820 examples to learn the encoder. 00:55:45.480 --> 00:55:46.770 But it does better as you get more 00:55:46.770 --> 00:55:48.260 examples like pretty sharply. 00:55:49.460 --> 00:55:52.200 If you use your pre trained model and 00:55:52.200 --> 00:55:54.260 linear probe you get the blue line. 00:55:54.260 --> 00:55:56.370 So if you have lots of examples that 00:55:56.370 --> 00:55:58.430 doesn't do as well, but if you have a 00:55:58.430 --> 00:56:00.956 few examples it can do quite well, it 00:56:00.956 --> 00:56:02.090 can be your best solution. 00:56:03.450 --> 00:56:05.146 And then the purple line is if you fine 00:56:05.146 --> 00:56:06.816 tune so you pre trained on Imagenet and 00:56:06.816 --> 00:56:09.360 then you fine tune to in this case C 00:56:09.360 --> 00:56:11.290 four 100 which is 100 different object 00:56:11.290 --> 00:56:16.160 classes and you that generally works 00:56:16.160 --> 00:56:16.940 the best. 00:56:18.040 --> 00:56:20.580 It generally outperforms the linear 00:56:20.580 --> 00:56:22.030 model, except when you have very, very 00:56:22.030 --> 00:56:23.590 few training examples per class. 00:56:24.200 --> 00:56:25.640 And then this is showing the same thing 00:56:25.640 --> 00:56:27.900 on a different data set, which is a 00:56:27.900 --> 00:56:29.990 much larger data set for place 00:56:29.990 --> 00:56:30.550 recognition. 00:56:32.590 --> 00:56:34.050 So it's a little late for it, but I'll 00:56:34.050 --> 00:56:36.180 do it anyway so we can take a quick 00:56:36.180 --> 00:56:38.029 break and if you would like, you can 00:56:38.030 --> 00:56:40.130 think about this question, which I'll 00:56:40.130 --> 00:56:41.500 answer after the break. 00:56:53.360 --> 00:56:55.420 You can do your own task as well. 00:57:00.350 --> 00:57:01.440 Challenges 1/2. 00:57:03.020 --> 00:57:05.430 Yeah, it says here you can choose pre 00:57:05.430 --> 00:57:07.010 selected challenge, select your own 00:57:07.010 --> 00:57:08.499 benchmark task or create your own 00:57:08.500 --> 00:57:09.290 custom task. 00:57:09.690 --> 00:57:11.080 Yeah. 00:57:13.000 --> 00:57:16.150 So the red one is if you use a randomly 00:57:16.150 --> 00:57:17.990 initialized network and then you just 00:57:17.990 --> 00:57:18.940 train a linear model. 00:57:21.780 --> 00:57:23.250 It's a worse this is error. 00:57:25.230 --> 00:57:25.520 Yeah. 00:57:48.800 --> 00:57:50.230 Just because you can't see it in the 00:57:50.230 --> 00:57:51.950 table but the first layer, they don't 00:57:51.950 --> 00:57:52.960 apply downsampling. 00:57:54.730 --> 00:57:57.480 So in the first block it's just like 00:57:57.480 --> 00:57:59.340 processing features without down 00:57:59.340 --> 00:57:59.840 sampling. 00:58:00.980 --> 00:58:02.876 Yeah, the table doesn't show where the 00:58:02.876 --> 00:58:04.450 downsampling's happening except through 00:58:04.450 --> 00:58:05.940 the size changing. 00:58:05.940 --> 00:58:09.470 But if you look here, it's like down 00:58:09.470 --> 00:58:10.970 sample falls, down sample falls. 00:58:11.550 --> 00:58:13.080 So it doesn't downsample in the first 00:58:13.080 --> 00:58:15.396 layer and then it down sample true, 00:58:15.396 --> 00:58:16.999 down sample true, down sample true. 00:58:32.470 --> 00:58:33.760 Maybe I can answer after. 00:58:36.060 --> 00:58:36.570 All right. 00:58:36.570 --> 00:58:39.670 So does anybody have like a simple 00:58:39.670 --> 00:58:41.590 explanation for this question? 00:58:41.590 --> 00:58:44.392 So why does when does each one have an 00:58:44.392 --> 00:58:46.593 advantage and why does it have an 00:58:46.593 --> 00:58:48.000 advantage, I think, in terms of the 00:58:48.000 --> 00:58:48.950 amount of training data? 00:58:58.690 --> 00:59:01.120 So you can think about this in terms of 00:59:01.120 --> 00:59:02.900 the bias variance tradeoff, right? 00:59:02.900 --> 00:59:04.420 So if you have a lot of data. 00:59:05.370 --> 00:59:07.752 Then you can afford to have a higher 00:59:07.752 --> 00:59:09.860 bias class or a lower bias classifier 00:59:09.860 --> 00:59:12.305 that has higher variance because the 00:59:12.305 --> 00:59:13.910 data will reduce that variance. 00:59:13.910 --> 00:59:16.290 Your ultimate variance depends on the 00:59:16.290 --> 00:59:17.690 complexity of the model as well as the 00:59:17.690 --> 00:59:18.570 amount of data you have. 00:59:19.480 --> 00:59:22.360 And so if you have very little data 00:59:22.360 --> 00:59:24.260 then linear probe might be your best 00:59:24.260 --> 00:59:26.680 solution because all your training is 00:59:26.680 --> 00:59:28.185 that last classification layer. 00:59:28.185 --> 00:59:30.630 So use your limited data to train just 00:59:30.630 --> 00:59:32.190 a linear model so. 00:59:33.210 --> 00:59:35.560 If you look at the blue curve, the blue 00:59:35.560 --> 00:59:38.100 curve starts to outperform when you 00:59:38.100 --> 00:59:40.130 have like less than 16 examples per 00:59:40.130 --> 00:59:40.760 class. 00:59:40.760 --> 00:59:42.770 Then it achieves the lower lowest 00:59:42.770 --> 00:59:43.090 error. 00:59:44.030 --> 00:59:45.900 So if you have very limited data, then 00:59:45.900 --> 00:59:47.420 just training the linear probe and 00:59:47.420 --> 00:59:49.140 trusting your encoding may be best. 00:59:50.980 --> 00:59:53.480 If you have like a pretty good amount 00:59:53.480 --> 00:59:54.060 of data. 00:59:54.720 --> 00:59:57.090 Then fine tuning is the best. 00:59:57.800 --> 00:59:59.833 Because you're starting with that 00:59:59.833 --> 01:00:01.590 initial solution from the encoder and 01:00:01.590 --> 01:00:03.410 allowing it to drift some, but not too 01:00:03.410 --> 01:00:03.680 much. 01:00:03.680 --> 01:00:05.400 So you're kind of constraining it based 01:00:05.400 --> 01:00:06.710 on the initial encoding that you 01:00:06.710 --> 01:00:07.720 learned from lots of data. 01:00:08.540 --> 01:00:13.400 And so the purple curve which is fine 01:00:13.400 --> 01:00:15.970 tuning works the best for like a big 01:00:15.970 --> 01:00:19.176 section of the this X axis, which is 01:00:19.176 --> 01:00:20.400 the amount of training data that you 01:00:20.400 --> 01:00:20.760 have. 01:00:21.670 --> 01:00:24.790 But if you have a ton of data, then 01:00:24.790 --> 01:00:26.850 there's no point fine tuning from some 01:00:26.850 --> 01:00:28.520 other data set if you have way more 01:00:28.520 --> 01:00:30.060 data than Imagenet for example. 01:00:30.690 --> 01:00:32.070 Then you should be able to train from 01:00:32.070 --> 01:00:34.530 scratch and optimize as possible and 01:00:34.530 --> 01:00:36.440 get a better encoding than if you just 01:00:36.440 --> 01:00:38.280 like kind of tie your encoding to the 01:00:38.280 --> 01:00:39.400 initial one from Imagenet. 01:00:40.260 --> 01:00:42.415 And so training from scratch can work 01:00:42.415 --> 01:00:44.663 the best if you have the most if you 01:00:44.663 --> 01:00:46.790 have a lot of data like Imagenet scale 01:00:46.790 --> 01:00:47.130 data. 01:00:52.680 --> 01:00:55.290 So I'm going to talk give you a sense 01:00:55.290 --> 01:00:57.960 of how detection works with these deep 01:00:57.960 --> 01:00:58.810 networks. 01:00:58.940 --> 01:00:59.470 01:01:00.530 --> 01:01:01.710 And actually I'm going to go a little 01:01:01.710 --> 01:01:02.260 bit out of order. 01:01:02.260 --> 01:01:03.450 Let me come back to that in just a 01:01:03.450 --> 01:01:03.740 second. 01:01:04.560 --> 01:01:05.720 Because. 01:01:07.660 --> 01:01:10.149 I want to show you what the 01:01:10.150 --> 01:01:11.990 visualization of what the network's 01:01:11.990 --> 01:01:12.520 learning. 01:01:12.520 --> 01:01:14.380 This applies to classification as well. 01:01:15.100 --> 01:01:17.020 So to create this visualization, these 01:01:17.020 --> 01:01:20.480 researchers they back propagate the 01:01:20.480 --> 01:01:21.960 gradients through the network so that 01:01:21.960 --> 01:01:24.300 they can see which pixels are causing a 01:01:24.300 --> 01:01:26.100 feature to be activated to have like a 01:01:26.100 --> 01:01:26.690 high value. 01:01:27.600 --> 01:01:28.900 And here they're showing the 01:01:28.900 --> 01:01:33.310 activations and the image patches that 01:01:33.310 --> 01:01:35.250 like strongly activated particularly 01:01:35.250 --> 01:01:35.870 features. 01:01:35.870 --> 01:01:38.040 So you can see that in the first layer 01:01:38.040 --> 01:01:40.360 of the network and this is 2014 before 01:01:40.360 --> 01:01:40.575 resna. 01:01:40.575 --> 01:01:42.530 So this is for like Alex, net style 01:01:42.530 --> 01:01:44.200 networks in this particular example. 01:01:44.840 --> 01:01:47.588 But in the early layers of the network, 01:01:47.588 --> 01:01:50.080 the network is basically representing 01:01:50.080 --> 01:01:51.110 color and edges. 01:01:51.110 --> 01:01:53.000 So like each of these three by three 01:01:53.000 --> 01:01:55.460 blocks are like patches that had high 01:01:55.460 --> 01:01:57.990 response to a particular feature. 01:01:57.990 --> 01:01:59.790 So one of them is just like green, 01:01:59.790 --> 01:02:01.180 another one is whether it's blue or 01:02:01.180 --> 01:02:03.640 orange, another one is like this, these 01:02:03.640 --> 01:02:04.430 bar features. 01:02:05.210 --> 01:02:06.849 And a lot of these actually look a lot 01:02:06.850 --> 01:02:10.280 like the filters that happened in early 01:02:10.280 --> 01:02:11.500 processing in the brain. 01:02:13.800 --> 01:02:14.380 01:02:15.130 --> 01:02:16.650 The brain doesn't do convolution 01:02:16.650 --> 01:02:18.890 exactly, but actually it's essentially 01:02:18.890 --> 01:02:21.220 the same does it does convolution by 01:02:21.220 --> 01:02:21.800 other means. 01:02:21.800 --> 01:02:24.070 Basically you can show like the. 01:02:24.790 --> 01:02:27.270 What sensitivity of neurons to 01:02:27.270 --> 01:02:27.840 stimulate? 01:02:29.060 --> 01:02:32.720 And the then this is layer two, so you 01:02:32.720 --> 01:02:34.918 start to get like texture patterns like 01:02:34.918 --> 01:02:36.026 this one. 01:02:36.026 --> 01:02:38.230 One node is responsive to these 01:02:38.230 --> 01:02:38.740 stripes. 01:02:39.370 --> 01:02:41.330 Another one to like these thin lines. 01:02:41.330 --> 01:02:43.770 Another one is to yellow with some 01:02:43.770 --> 01:02:45.180 corner features. 01:02:46.340 --> 01:02:47.810 So they're like kind of like texture 01:02:47.810 --> 01:02:50.170 and low to mid level features and the 01:02:50.170 --> 01:02:50.700 next layer. 01:02:52.460 --> 01:02:53.940 And then in the next layer, it's like 01:02:53.940 --> 01:02:55.615 more complex patterns like this one. 01:02:55.615 --> 01:02:58.200 It's responsible to like responsive to 01:02:58.200 --> 01:03:00.340 like grids in different orientations as 01:03:00.340 --> 01:03:01.940 well as like grids of these circles. 01:03:03.860 --> 01:03:07.050 This one is starting is responsive to 01:03:07.050 --> 01:03:08.410 people's torso and head. 01:03:09.750 --> 01:03:11.979 Then there's like some that are like 01:03:11.980 --> 01:03:13.610 text, so they're starting to become 01:03:13.610 --> 01:03:15.638 more objectivity in what they're 01:03:15.638 --> 01:03:17.420 responsive, what these network nodes 01:03:17.420 --> 01:03:18.350 are responsive to. 01:03:19.220 --> 01:03:20.650 And you can see that for example. 01:03:21.380 --> 01:03:23.955 These ones that are firing on these 01:03:23.955 --> 01:03:26.256 grids, the active part are the lines 01:03:26.256 --> 01:03:27.960 are the lines on the grids. 01:03:27.960 --> 01:03:30.310 So it's actually and for these people 01:03:30.310 --> 01:03:32.550 it's responding heavily to their faces. 01:03:35.000 --> 01:03:36.370 And then as you go deeper into the 01:03:36.370 --> 01:03:39.450 network, you start to get like object 01:03:39.450 --> 01:03:41.023 representations or object part 01:03:41.023 --> 01:03:41.499 representations. 01:03:41.500 --> 01:03:43.806 So there's a node for dogheads and 01:03:43.806 --> 01:03:48.205 there's the like curved part of circles 01:03:48.205 --> 01:03:51.370 and animal feed and birds that are 01:03:51.370 --> 01:03:51.910 swimming. 01:03:52.570 --> 01:03:56.769 And birds that are standing so and then 01:03:56.770 --> 01:03:58.470 this is layer four and then layer 5 01:03:58.470 --> 01:04:00.140 again is like more parts. 01:04:00.140 --> 01:04:02.090 So as you go through the network the 01:04:02.090 --> 01:04:04.373 representation start to represent more 01:04:04.373 --> 01:04:07.110 like objects, object level features 01:04:07.110 --> 01:04:08.280 where the earlier layers are 01:04:08.280 --> 01:04:09.970 representing like color and texture. 01:04:11.860 --> 01:04:14.560 There's lots of different ways of doing 01:04:14.560 --> 01:04:15.770 these visualizations that are 01:04:15.770 --> 01:04:17.270 interesting to look at, but that's just 01:04:17.270 --> 01:04:18.500 to give you like some sense. 01:04:52.410 --> 01:04:54.052 Yes, I think the question is like 01:04:54.052 --> 01:04:55.590 whether you should whether you should 01:04:55.590 --> 01:04:56.895 make use of color information because 01:04:56.895 --> 01:04:58.940 it's not that predictive I guess 01:04:58.940 --> 01:04:59.410 earlier. 01:04:59.410 --> 01:05:04.220 So in the when I was like in 2004 often 01:05:04.220 --> 01:05:05.690 people would train face detectors on 01:05:05.690 --> 01:05:07.200 grayscale images for that reason 01:05:07.200 --> 01:05:08.300 because they said color can be 01:05:08.300 --> 01:05:10.373 misleading and it's really depends on 01:05:10.373 --> 01:05:11.345 the amount of data. 01:05:11.345 --> 01:05:14.265 So the networks can learn like how much 01:05:14.265 --> 01:05:16.040 I trust color essentially. 01:05:16.040 --> 01:05:18.460 And so if you have like lots of data or 01:05:18.460 --> 01:05:20.280 you train a good encoder with Imagenet 01:05:20.280 --> 01:05:22.240 then there's no need to like. 01:05:22.300 --> 01:05:24.010 Grayscale your images. 01:05:24.010 --> 01:05:25.415 You just give the network essentially 01:05:25.415 --> 01:05:26.910 the choice of whether to use that 01:05:26.910 --> 01:05:27.700 information or not. 01:05:28.600 --> 01:05:29.930 So that would be like the current 01:05:29.930 --> 01:05:31.560 thinking on that, but it's a good 01:05:31.560 --> 01:05:32.110 question. 01:05:37.020 --> 01:05:37.430 All right. 01:05:37.430 --> 01:05:39.270 So I'm going to talk a little bit about 01:05:39.270 --> 01:05:41.590 object detection and I'll probably have 01:05:41.590 --> 01:05:44.200 to pick up, pick up some of this at the 01:05:44.200 --> 01:05:45.410 next class, but that's fine. 01:05:46.710 --> 01:05:48.880 So this is how object detection works 01:05:48.880 --> 01:05:49.420 in general. 01:05:49.420 --> 01:05:50.397 It's called like this. 01:05:50.397 --> 01:05:52.120 It's called a statistical template 01:05:52.120 --> 01:05:54.410 approach to object detection where you 01:05:54.410 --> 01:05:56.750 basically propose some windows where 01:05:56.750 --> 01:05:58.662 you think the object might be and this 01:05:58.662 --> 01:06:00.680 can either be like a very brute force 01:06:00.680 --> 01:06:03.589 dumb extract every patch, or you can 01:06:03.590 --> 01:06:06.480 use some segmentation methods to get 01:06:06.480 --> 01:06:08.630 like groups of pixels that have similar 01:06:08.630 --> 01:06:10.920 colors and then put boxes around those. 01:06:11.610 --> 01:06:13.170 But either way, you get a set of 01:06:13.170 --> 01:06:15.250 locations in the image that's like a 01:06:15.250 --> 01:06:18.374 bounding box A2 corners in the image 01:06:18.374 --> 01:06:21.310 that you think that the object some 01:06:21.310 --> 01:06:22.995 object of interest might be inside of 01:06:22.995 --> 01:06:23.510 that box. 01:06:24.550 --> 01:06:26.290 You extract the features within that 01:06:26.290 --> 01:06:26.660 box. 01:06:26.660 --> 01:06:28.705 So you could use hog features which we 01:06:28.705 --> 01:06:31.235 talked about for SVM which all Triggs 01:06:31.235 --> 01:06:32.960 could be these horror. 01:06:32.960 --> 01:06:34.756 These are called hard wavelet features 01:06:34.756 --> 01:06:36.760 that we use with boosting for face 01:06:36.760 --> 01:06:39.500 detection by Viola Jones or CNN 01:06:39.500 --> 01:06:40.930 features which we just talked about. 01:06:41.930 --> 01:06:44.110 Then you classify those features 01:06:44.110 --> 01:06:45.780 independently for each patch. 01:06:46.780 --> 01:06:48.490 And then you have some post process, 01:06:48.490 --> 01:06:50.390 because neighboring patches might have 01:06:50.390 --> 01:06:51.680 similar scores because they're 01:06:51.680 --> 01:06:53.520 overlapping and so you want to take the 01:06:53.520 --> 01:06:54.570 one with the highest score. 01:06:55.970 --> 01:06:57.805 And this is just generally how many 01:06:57.805 --> 01:06:59.510 many object detection methods work. 01:07:00.940 --> 01:07:04.570 So the first like big foray into deep 01:07:04.570 --> 01:07:07.540 networks with this approach was R CNN 01:07:07.540 --> 01:07:08.470 by Girshick Adal. 01:07:09.610 --> 01:07:11.870 And you take an input image. 01:07:11.870 --> 01:07:14.300 They use this method to extract boxes 01:07:14.300 --> 01:07:15.910 called selective search. 01:07:15.910 --> 01:07:17.480 Details aren't really that important, 01:07:17.480 --> 01:07:19.780 but you get around 2000 different 01:07:19.780 --> 01:07:21.400 windows that might contain objects. 01:07:22.390 --> 01:07:26.110 You warp them into a 224 by 24 patch or 01:07:26.110 --> 01:07:28.270 I guess 227 by 27. 01:07:28.270 --> 01:07:29.250 This is just because of. 01:07:29.250 --> 01:07:31.480 That's the size of like the Imagenet 01:07:31.480 --> 01:07:34.880 classifier would process, including 01:07:34.880 --> 01:07:35.410 some padding. 01:07:37.020 --> 01:07:39.150 Then you put this through your image 01:07:39.150 --> 01:07:41.628 net classifier and extract the 01:07:41.628 --> 01:07:45.600 features, and then you train a SVM to 01:07:45.600 --> 01:07:47.763 classify those features into each of 01:07:47.763 --> 01:07:48.890 the classes of interests. 01:07:48.890 --> 01:07:50.690 Like is it an airplane, is it a person, 01:07:50.690 --> 01:07:51.710 is it a TV monitor? 01:07:53.390 --> 01:07:56.550 That this was like the first like 01:07:56.550 --> 01:08:00.280 really amazing demonstration of fine 01:08:00.280 --> 01:08:00.620 tuning. 01:08:00.620 --> 01:08:02.560 So they would also fine tune this CNN 01:08:02.560 --> 01:08:04.605 classifier to do better, so they would 01:08:04.605 --> 01:08:06.250 fine tune it to do this task. 01:08:06.250 --> 01:08:08.160 So this was like very surprising at the 01:08:08.160 --> 01:08:09.730 time that you could take an image 01:08:09.730 --> 01:08:11.984 classification method and then you fine 01:08:11.984 --> 01:08:13.836 tune it, just set a lower learning rate 01:08:13.836 --> 01:08:15.740 and then adapt it to this new task 01:08:15.740 --> 01:08:16.800 where you didn't have that much 01:08:16.800 --> 01:08:17.420 training data. 01:08:17.420 --> 01:08:18.550 Relatively. 01:08:18.550 --> 01:08:20.120 If you train it from scratch it doesn't 01:08:20.120 --> 01:08:20.800 work that well. 01:08:20.800 --> 01:08:23.255 You have to start with the Imagenet pre 01:08:23.255 --> 01:08:23.750 training. 01:08:23.850 --> 01:08:25.890 Classifier and then go from there and 01:08:25.890 --> 01:08:27.630 then they got like amazing results. 01:08:30.180 --> 01:08:32.520 The next step was like there's a 01:08:32.520 --> 01:08:34.170 glaring inefficiency here, which is 01:08:34.170 --> 01:08:36.290 that you extract each patch and then 01:08:36.290 --> 01:08:38.172 pass that patch through the network. 01:08:38.172 --> 01:08:40.682 So you have to you have 2000 patches, 01:08:40.682 --> 01:08:42.613 and each of those 2000 patches you have 01:08:42.613 --> 01:08:44.760 to put through your network in order to 01:08:44.760 --> 01:08:45.410 detection. 01:08:45.410 --> 01:08:47.150 So super, super slow. 01:08:48.120 --> 01:08:50.010 So their next step is that you apply 01:08:50.010 --> 01:08:52.240 the network to the image and then you 01:08:52.240 --> 01:08:54.270 just extract patches within the feature 01:08:54.270 --> 01:08:58.776 maps at like a later layer in the 01:08:58.776 --> 01:08:59.449 network. 01:09:00.230 --> 01:09:02.430 And so that just made it really, really 01:09:02.430 --> 01:09:02.940 fast. 01:09:02.940 --> 01:09:04.420 You get similar performance. 01:09:05.420 --> 01:09:07.440 They also added something where you 01:09:07.440 --> 01:09:09.000 don't trust that initial window 01:09:09.000 --> 01:09:09.790 exactly. 01:09:09.790 --> 01:09:11.405 You predict something that like adjusts 01:09:11.405 --> 01:09:13.540 the corners of the box so you get 01:09:13.540 --> 01:09:15.320 better localization. 01:09:16.830 --> 01:09:18.870 That gave it 100 X speedup, where the 01:09:18.870 --> 01:09:21.395 first system ran at like 50. 01:09:21.395 --> 01:09:23.490 It could be as slow as. 01:09:24.570 --> 01:09:27.990 50 seconds per frame, as fast as 20 01:09:27.990 --> 01:09:28.780 frames per second. 01:09:29.740 --> 01:09:31.910 But on average, 100X speedup. 01:09:34.120 --> 01:09:39.150 That was fast CNN faster our CNN is 01:09:39.150 --> 01:09:41.330 that instead of using that selective 01:09:41.330 --> 01:09:43.020 search method to propose Windows, you 01:09:43.020 --> 01:09:45.370 also learn to propose like the boxes 01:09:45.370 --> 01:09:46.760 inside the image where you think the 01:09:46.760 --> 01:09:47.450 object might be. 01:09:48.080 --> 01:09:49.300 And they use what's called like a 01:09:49.300 --> 01:09:50.800 region proposal network, a small 01:09:50.800 --> 01:09:51.840 network that does that. 01:09:51.840 --> 01:09:53.230 So it takes some intermediate 01:09:53.230 --> 01:09:56.290 representation from the encoder, and 01:09:56.290 --> 01:09:58.520 then it predicts for each position what 01:09:58.520 --> 01:10:00.310 are some boxes around that might 01:10:00.310 --> 01:10:02.510 contain the object of interest or an 01:10:02.510 --> 01:10:03.470 object of interest. 01:10:04.490 --> 01:10:07.320 And then they use that for 01:10:07.320 --> 01:10:08.110 classification. 01:10:08.740 --> 01:10:10.730 And then this gave similar accuracy to 01:10:10.730 --> 01:10:12.550 the previous method, but gave another 01:10:12.550 --> 01:10:13.370 10X speedup. 01:10:13.370 --> 01:10:14.850 So now it's like pretty fast. 01:10:16.780 --> 01:10:19.890 And then the final one is mask R CNN, 01:10:19.890 --> 01:10:21.860 which is still really widely used 01:10:21.860 --> 01:10:22.220 today. 01:10:24.160 --> 01:10:26.470 And it's essentially the same network 01:10:26.470 --> 01:10:28.690 as faster R CNN, but they added 01:10:28.690 --> 01:10:29.900 additional branches to it. 01:10:30.940 --> 01:10:34.860 So in faster R CNN, for every initial 01:10:34.860 --> 01:10:38.050 window you would predict a class score 01:10:38.050 --> 01:10:39.820 for each of your classes and it could 01:10:39.820 --> 01:10:41.190 be their background or one of those 01:10:41.190 --> 01:10:41.690 classes. 01:10:42.350 --> 01:10:44.510 And you would predict a refined box to 01:10:44.510 --> 01:10:46.090 better focus on the object. 01:10:46.700 --> 01:10:48.230 And they added to it additional 01:10:48.230 --> 01:10:51.750 branches 1 predicts a pixel whether 01:10:51.750 --> 01:10:53.640 each pixel is on the object or not. 01:10:53.640 --> 01:10:55.543 So this is like the predicted mask for 01:10:55.543 --> 01:10:56.710 a car for example. 01:10:57.780 --> 01:11:02.030 And it's in a predicts it in a 28 by 28 01:11:02.030 --> 01:11:03.620 windows, so a small patch that then 01:11:03.620 --> 01:11:05.930 gets resized into the original window. 01:11:06.880 --> 01:11:09.310 And they also predict key points for 01:11:09.310 --> 01:11:09.985 people. 01:11:09.985 --> 01:11:13.990 And that's just like again a pixel map 01:11:13.990 --> 01:11:15.840 where you predict whether each pixel is 01:11:15.840 --> 01:11:17.778 like the left eye or whether each pixel 01:11:17.778 --> 01:11:19.569 is the right eye or the. 01:11:20.740 --> 01:11:22.530 Or the left hip or right hip and so on. 01:11:23.570 --> 01:11:25.310 And that gives you these key point 01:11:25.310 --> 01:11:25.910 predictions. 01:11:26.790 --> 01:11:29.050 So with the same network you're doing, 01:11:29.050 --> 01:11:31.660 detecting objects, segmenting out those 01:11:31.660 --> 01:11:34.040 objects or labeling their pixels and 01:11:34.040 --> 01:11:35.480 labeling the parts of people. 01:11:36.740 --> 01:11:39.130 And that same method was, at the time 01:11:39.130 --> 01:11:41.226 of release, the best object detector, 01:11:41.226 --> 01:11:43.464 the best instance segmentation method, 01:11:43.464 --> 01:11:46.060 and the best person keypoint detector. 01:11:47.100 --> 01:11:48.280 And it's still one of the most 01:11:48.280 --> 01:11:48.980 effective. 01:11:49.710 --> 01:11:51.900 So these are some examples of how it 01:11:51.900 --> 01:11:52.450 performs. 01:11:53.380 --> 01:11:56.770 So up here there's it's detecting 01:11:56.770 --> 01:11:59.715 Donuts and segmenting them, so all the 01:11:59.715 --> 01:12:01.030 different colors correspond to 01:12:01.030 --> 01:12:02.210 different doughnut regions. 01:12:03.400 --> 01:12:05.539 There's horses and know how, like it 01:12:05.540 --> 01:12:06.220 knows that. 01:12:06.220 --> 01:12:08.180 It's not saying that the fence is a 01:12:08.180 --> 01:12:08.570 horse. 01:12:08.570 --> 01:12:10.310 It segments around the fence. 01:12:11.650 --> 01:12:15.650 There's people here in bags like 01:12:15.650 --> 01:12:18.540 handbag, traffic lights. 01:12:18.540 --> 01:12:20.565 This is in the Coco data set, which has 01:12:20.565 --> 01:12:21.340 80 classes. 01:12:22.330 --> 01:12:22.710 Chairs. 01:12:22.710 --> 01:12:24.080 These are not ground truth. 01:12:24.080 --> 01:12:25.210 These are the predictions of the 01:12:25.210 --> 01:12:25.780 network. 01:12:25.780 --> 01:12:28.130 So it's really accurate at segmenting 01:12:28.130 --> 01:12:29.180 and detecting objects. 01:12:29.810 --> 01:12:31.420 And then these numbers are the scores, 01:12:31.420 --> 01:12:33.010 which are probably hard to see from the 01:12:33.010 --> 01:12:34.160 audience, but they're. 01:12:35.290 --> 01:12:36.280 They tend to be pretty high. 01:12:37.540 --> 01:12:38.730 Here's just more examples. 01:12:39.500 --> 01:12:42.000 So you this thing can detect lots of 01:12:42.000 --> 01:12:43.900 different classes and segment them and 01:12:43.900 --> 01:12:44.760 it can also. 01:12:45.400 --> 01:12:46.840 Predict the. 01:12:47.850 --> 01:12:48.970 The parts of the people. 01:12:50.850 --> 01:12:53.510 So the poses of the people that are 01:12:53.510 --> 01:12:54.985 then also segmented out. 01:12:54.985 --> 01:12:56.649 So this is used in many many 01:12:56.650 --> 01:12:58.600 applications like my non vision 01:12:58.600 --> 01:13:01.570 researchers, because it's very good at 01:13:01.570 --> 01:13:03.640 detecting people, segmenting people, 01:13:03.640 --> 01:13:06.529 finding their parts as well as you can 01:13:06.530 --> 01:13:09.005 adapt it to train, you can retrain it 01:13:09.005 --> 01:13:10.400 to do many other kinds of object 01:13:10.400 --> 01:13:10.930 detection. 01:13:19.640 --> 01:13:21.420 Sorry, I will finish this because I've 01:13:21.420 --> 01:13:22.290 got 2 minutes. 01:13:22.420 --> 01:13:22.900 01:13:23.700 --> 01:13:25.100 So the last thing I just want to 01:13:25.100 --> 01:13:27.540 briefly mention is, so those are like 01:13:27.540 --> 01:13:30.150 resna is like a real staple of deep 01:13:30.150 --> 01:13:32.183 learning and computer vision, one of 01:13:32.183 --> 01:13:34.080 the other kinds of architectures that's 01:13:34.080 --> 01:13:35.900 really widely used if you're trying to 01:13:35.900 --> 01:13:38.100 produce images or image maps, like 01:13:38.100 --> 01:13:39.955 label every pixel in the image into sky 01:13:39.955 --> 01:13:40.910 or tree or building. 01:13:41.600 --> 01:13:43.750 Is this thing called a unit 01:13:43.750 --> 01:13:44.510 architecture? 01:13:45.740 --> 01:13:47.240 So in the unit architecture. 01:13:48.960 --> 01:13:52.510 You process the image you start with 01:13:52.510 --> 01:13:54.090 like a high resolution image, you 01:13:54.090 --> 01:13:57.360 process it and justice like for Resnet 01:13:57.360 --> 01:13:59.930 or other architectures, you downsample 01:13:59.930 --> 01:14:01.840 it while deepening the features. 01:14:01.840 --> 01:14:03.502 So you make the image smaller and 01:14:03.502 --> 01:14:05.124 smaller spatially while making the 01:14:05.124 --> 01:14:06.260 features deeper and deeper. 01:14:07.610 --> 01:14:09.130 And then eventually you get to a big 01:14:09.130 --> 01:14:11.150 long vector of features, just like for 01:14:11.150 --> 01:14:12.391 Resnet and other architectures. 01:14:12.391 --> 01:14:15.439 And then you upsample it back into an 01:14:15.439 --> 01:14:17.769 image map and when you upsample it, you 01:14:17.770 --> 01:14:19.350 have skip connections from this 01:14:19.350 --> 01:14:21.362 corresponding layer of detail. 01:14:21.362 --> 01:14:23.670 So as it's like bringing it back into 01:14:23.670 --> 01:14:27.210 an image size output, you're adding 01:14:27.210 --> 01:14:29.970 back the features from the detail that 01:14:29.970 --> 01:14:31.840 was obtained when you down sampled it. 01:14:31.840 --> 01:14:34.313 And this allows you to like upsample 01:14:34.313 --> 01:14:35.650 back into high detail. 01:14:35.650 --> 01:14:37.000 So this produces. 01:14:37.000 --> 01:14:37.770 This is used for. 01:14:37.860 --> 01:14:39.400 Pics to pics or. 01:14:40.160 --> 01:14:42.400 Image segmentation methods where you're 01:14:42.400 --> 01:14:43.900 trying to produce some output or some 01:14:43.900 --> 01:14:45.050 value for each pixel. 01:14:47.040 --> 01:14:48.240 Just where it's being aware of what 01:14:48.240 --> 01:14:49.210 this is, but. 01:14:49.450 --> 01:14:53.530 I'm not going to go into much detail. 01:14:54.270 --> 01:14:54.530 Right. 01:14:54.530 --> 01:14:58.100 So in summary, we learned that the 01:14:58.100 --> 01:15:00.530 massive Imagenet data set, it was a key 01:15:00.530 --> 01:15:01.770 ingredient, the deep learning 01:15:01.770 --> 01:15:02.280 breakthrough. 01:15:03.220 --> 01:15:06.230 We saw how resonates used skip 01:15:06.230 --> 01:15:07.200 connections. 01:15:07.200 --> 01:15:08.800 It uses data augmentation and batch 01:15:08.800 --> 01:15:09.440 normalization. 01:15:09.440 --> 01:15:10.900 These are also commonly used in many 01:15:10.900 --> 01:15:11.860 other architectures. 01:15:12.940 --> 01:15:15.000 Really important is that you can pre 01:15:15.000 --> 01:15:17.180 train a model on a large data set, for 01:15:17.180 --> 01:15:19.220 example Imagenet and then use that pre 01:15:19.220 --> 01:15:20.420 trained model as what's called a 01:15:20.420 --> 01:15:22.220 backbone for other tasks where you 01:15:22.220 --> 01:15:24.935 either apply it as or allow it to tune 01:15:24.935 --> 01:15:26.330 a little bit in the training. 01:15:27.570 --> 01:15:30.240 And then I showed you a little bit 01:15:30.240 --> 01:15:32.570 about mask R CNN which samples the 01:15:32.570 --> 01:15:33.980 patches and the feature maps and 01:15:33.980 --> 01:15:36.350 predicts boxes, the object region and 01:15:36.350 --> 01:15:36.870 key points. 01:15:37.850 --> 01:15:39.560 And then finally the main thing to 01:15:39.560 --> 01:15:41.350 know, just be aware of the unit which 01:15:41.350 --> 01:15:42.860 is a common architecture for 01:15:42.860 --> 01:15:45.030 segmentation or image generation. 01:15:46.750 --> 01:15:49.340 So thanks very much and on Tuesday I 01:15:49.340 --> 01:15:51.380 will talk about language and word 01:15:51.380 --> 01:15:53.570 representations. 01:15:53.570 --> 01:15:54.400 Have a good weekend. 01:15:58.190 --> 01:15:58.570 Welcome.