for someone like me who has ventured into neural nets recently, this explanation is a boon. IT was like listening to classics. Legendary paper and equally awesome explanation.
I really wanted to drop you a line that I really, really enjoyed your paper walkthrough; super informative and entertaining! Thank you so much for uploading this! :)
This is probably one of the better videos on these classic research papers on RU-vid. I've seen some terrible explanations but you did pretty well. Good job!
I was doing something similar for a few decades before this paper came out (no ReLU on the stage output, though). I was engaged in studies in layer by layer training, and the argument for me was "why spend all that time generating a good output for layer k, just to distort it in layer k+1?" Also, I think the physicist in me liked the notion of nonlinear perturbation of a linear model, since linear models work really well a lot of the time (MNIST, I'm looking at you). At any rate, this approach worked quite well in the time series signal processing I was doing, and when the paper came out, I read with relish to see what else they had found that was new. Unfortunately, like you I found that underneath the key idea was a heap of tricks to make the whole thing hang together which seemed to obscure how much was ResNet and how much was tricks.
Really enjoyed this video! I think going through these older paper that had lasting impact for multiple years is really a great insight especially to those who are fairly new to the field like me
"Sadly, the world has taken the ResNet, but the world hasn't all taken the research methodology of this paper." I really appreciate your picks are not only those papers surpassing the performance of the state of the art, but also those with intriguing insights or papers inspiring us by their ways of conducting experiments and testing hypotheses. Most vanish, but residual, as it moves forward.
COOL ! To discuss those classics. A formidable tribute to the writers and a great way to emphasize their contributions to the history of Artificial Intelligence.
Thanks for this videos the classic series, not all of us have masters or PHD degree, this classic papers help us to understand the main and core ideas of deep learning, papers that important and push fordward the field.
Yannic - you are doing a superb job. Your quality content has "lower dopamine rush effect". Thus, it wud not be viral, but with time you would be a force to reckon with. Not many can explain with so much clarity, depth & speed(daily 1 paper). I have one request. If you can create an ACTIVE mapping of paper with CITATIONS(and similar metrics) so that I get to choose the MOST RELEVANT PAPERS to watch. It would be a great time saver & drastically improve views on better metric videos :) .
Thanks. this was fun. I knew some of it but you put it on context. Please do more of these classics. If you can, maybe something on UNET/fully convolutional basic papers.
im struggling to understand papers, but your explanation to me it really hand held me to grasp this particular paper. For that to me you are awesome. Thank you so much
Great video! I think there is a lot of value on reviewing old papers when they a cited all the time by the new ones. That is exactly the case of ResNets.
This is a great series. I'm a very experienced software and hardware engineer who's just now getting serious about learning about ML and feel learning and the whole space. So, what really helps me at this point is not NN 101 but what is the landscape, what do all the acronyms mean, what is the relative importance of various ideas and techniques. This review of classic material is extremely helpful: it paints a picture of the world and helps me put things in their places in my mental model. Then I can dive deeper when I see something important for my current tasks and needs. Keep these coming!
I like how you have highlighted that if there is a small architecture exists that can solve a problem, residual connections will help discover it from within a larger architecture - I think this is a great explanation of the power of residual connections. This has two nice implications. I do not need to worry that I should exactly find how many layers are appropriate to capture. I can start with a supersized architecture and let training reduce to the subset that is needed! Let data carve out the subnetwork architecture. Secondly, even if the subnetwork is small, it is harder to directly train a small network. Easier to train a larger network with more degrees of freedom which functionally reduces to the smaller network. One can distill later.
Revisiting classic paper is SO NICE for new people enter into the field to understand the history of the million tricks that get automatically applied nowadays.
Revisiting the classics which had massively changed and forged the direction for DL research is so fun. Loved the way you explained the things. So cool. Thanks a lot :)
Would love to see papers like these which have used unique tricks to train, I request you do more videos on paper which solves the problem of training neural networks, tips and tricks and why they work. Why local response normalisation works, what's the best way to initialise your network layers for a vision task, for a NLP task. In a nutshell what works and why.🙏
Very enjoyable, insight filled presentation, Yannic, thanks! It almost seems like residual connections allow the network to only use the layers that dont corrupt the insight. Since every fully connected or convolutional layer is a destructive operation (reduction) of its inputs, signal may get distorted beyond recovery over a few blocks. By having a sideline crosswire where not only the original input but any derived computation can potentially be preserved at each step, network is freed from the 'tyranny of tranformation'. :) Both the paper and Yannic highlight the idea that - the goal shifts from 'deriving new insights from data' to 'preserving input as long (deep) as needed' - while all other types of layers in a network distort information or derive inferences from data, the residual connection allows preserving information and protecting it from being automatically distorted, so that any information can be safely copied over to any later layer.
I loved this paper. Resnets are still cool. Nowadays there are a more complicated versions of these nets but the ideas still pretty much hold these days. Nice video by the way.
I disagree with the assertion that the layers are learning “smaller” functions in ResNets. The results cited to support this claim, that the activations of the layers in the ResNets are larger than those in comparable feed-forward networks, can be caused by small weights and large biases, which L-2 regularization would encourage since it only operates on weights and not biases. The average magnitude of the weights in a layer have no relation to the complexity of the function they encode, since the weights of a layer can simply be scaled down without drastically changing this function. Moreover, in their paper on the Lottery Ticket Hypothesis, Frankle et al. find that ResNets are generally less compressible than feed-forward networks, meaning the functions they encode are more complex than in comparable feed-forward networks.
24:06 I think LeNet also did something similar but my memory fades. . Legendary paper. Great work. Too bad, I think in last two years we havent seen any major breakthroughs.
Another Great one! Would like to request if a review is possible on angular losses especially ArcFace, as it has begun being adopted for multiple classification tasks as another *classic* review. Thanks!
What is inception-net hypothesis?? In xception-net paper, the author explained the hypothesis of inception-net. But I couldn't grasp it fully and get lost a bit. Can you explain that??
Thank you very much for the explanation! I'm just starting to use the pretrained nets I wondered how could I improve the performance of my models, and this video cleared many doubts I had. Keep up the amazing work!
Little question about the connections when the shape changes: a simple 1x1 convolution can give the right depth but the feature maps would still be the original size. So I assume the 1x1 convolutions are also with stride 2?
If questions due to the parameters of the ResNet. As far as I understood it you concatenate the input with the output of another layer. This enables you to train more stable networks. Why does this lead to fewer parameters than the VGG? I would suggest that this is the case because you perform the more costly operations (more filters) on layers that are already reduced in their dimensions due to the stride? Is this correct?
i dont understand from the starting of the quoted statement (which I will write) upto 9:28, you are saying, "instead of learning to transform X via neural networks to X, which is an identity function, why don't we have X stay X and then learn whatever we need to change?" can you explain me this part with some analogy? I am beginner here. Thanks !!
I love how you will *not* review papers based on impact, except when you do :D JK, please mix in more [classic] papers, or whatever else you feel like - just keep the drive for ML. Is's contagious! 💦 An idea: combined review/your take of a whole class of models (eg. MobileNet and its variants &| YOLO variants)
In 3.4 - Implimentation, it says that they use BN after each conv layer and before the activation. Does this stand true for ResNet50+? In the bottleneck blocks, do they add BN after the first 1x1 conv layer and then the 3x3 and lastly the 1x1 again? Or was the Implementation part, discussing the ResNet34 structure?
I was wondering if there is any consensus now on why ResNet works so well? The common answer seems to be that the residual connections help avoid the vanishing gradient problem, but in the paper the authors argue that they don't think this is the case!
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