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Really, stop treating deep learning like Restricted Boltzmann Machines and Convolutional Neural Networks will solve all of your image classification woes.
And yes, they are capable of tremendous classification accuracy…provided that they are applied to the right type of problem.
From clustering, to forming a bag-of-words model, to soft codeword assignment, to learning distance metrics, to dimensionality reduction, to classification, regression (i.e.
That all said, if you are working with computer vision, you’ll also likely be utilizing some sort of machine learning. In terms of deep nets, computer vision and machine learning become even more entwined — look no farther than convolutional neural networks where we try to learn a set of kernels. With the rise and fall of machine learning, the tide will thus affect computer vision as well.
But then, Minsky and Papert’s 1969 publication effectively stagnated research in neural nets for almost a decade, demonstrating that the Perceptron could not solve the exclusive-or (XOR) problem. Luckily, the backpropagation algorithm and the research by Rumelhart (1986) and Werbos (1974) were able to bring back the neural net from what could have been an untimely demise. Arguably, without the contribution of these researchers, deep learning may have very well never existed. In the mid-90’s Cortes and Vapnik published their seminal Support-vector networks paper.
Building on the work of Amit and Geman (1997), Ho (1998), and Dietterich (2000), the late Leo Brieman contributed his Random Forests paper to the machine learning community in 2001. We hopped on the bandwagon again, loaded up a bunch of trees, threw in our shovels, and headed off to the closet nursery to setup camp. And to this day I still find myself slightly biased towards ensemble and forest based methods.
Instead, what we have is an amazing, incredible set of algorithms with both theoretical assumptions and empirical evidence, demonstrating they are capable of solving a certain subset of classification problems. However, the latest article by Google, Intriguing properties of neural networks, has suggested there is a gaping hole lurking in every deep neural net. And if these small changes in images (that are again, for all intents and purposes, completely undetectable to the human eye) can lead to performance completely falling off a cliff, what does that imply for real-world datasets? There is some incredible researching going on right now, and I personally get excited over Convolutional Neural Nets — I think for the next five years Convolutional Neural Nets will continue to dominate in certain image classification challenges, such as ImageNet.
I also hope the deep learning field stays active (I believe it will), because no matter what, our research and insights gained from studying deep nets will only help us create an even better approach years from now.
Instead, we need to spend a lot more time thinking about the actual problem we are trying to solve instead of throwing a bunch of algorithms at the problem and seeing what sticks. In my next post, I’ll show you how only a single pixel shift in an image can kill your Restricted Boltzmann Machine performance. Enter your email address below to get my free 11-page Image Search Engine Resource Guide PDF. This reminds me of how in fighting games when a new technique is discovered nearly everyone tries to use it in every situation possible to see how good it is.
I agree with you when saying that not being able to publish papers with negative results leads to a lot of shortcomings.
However, you could take the same approach and generate adversarial images specifically for SVMs. My prediction is, that at some point we will get deep higher level reasoning, and we will move away from pure feed forward networks.
My opinion on any classification model, whether HMM, deep learning, hybrid classification, etc. With your recent experiments with deep learning, will you be re-visiting this particular post?

It has become obvious in the past 12 months that deep learning has failed in the initial focus on unsupervised learning but has triumphed with supervised learning.
New companies are appearing that address vertical sectors by essentially hoovering up vast amounts of labeled data, running deep learning on EC2 gpu’s and then presenting the classifications (patterns). That all said, I have done a few tutorial posts on this blog regarding deep learning, including setting up an Amazon EC2 instance to utilize the GPU so you might want to check those out. What if we do not use raw pixels at all and instead only use some higher abstraction of image data ? One of the main benefits of deep learning is the ability to learn abstract representations of the data without using hand-tuned features such as SIFT and HOG. I know there a number of pretrained networks available for usage which are trained on large amounts of data, therefore giving very good results.
The lectures already taken can be uploaded various formats as in word sheet, videos, audio clips, powerpoint presentation etc. A year-long festival designed to foster relationships and to showcase how the arts connect people and ideas, activate cultural discourse, empower life-long learning, and integrate multiple disciplines in distinctive modes of inquiry.
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As summer approaches, it’s appropriate that Rockbrook Elementary fourth-graders spent time learning how to swim as part of Lewisville ISD’s swim program. Throughout the course of the school year, fourth-graders across the district have had, or will have, an opportunity to participate in the program. The swim program exemplifies one way LISD is working to provide comprehensive learning opportunities that will help fulfill its core belief that an educated citizenry is essential for a prosperous society.
In all honesty, I support deep learning research, I support the findings, and I believe that by researching deep learning we can only further improve our classification approaches and develop better methods. We all know that research is iterative.
But these algorithms are not silver bullets, they are not magic pills, and they are not tools in a toolbox — they are methodologies backed by rational thought processes with assumptions regarding the datasets they are applied to.
Unless you are doing some very strict forms of image processing, you can’t have computer vision without some sort of machine learning. Furthermore, the authors argued that we did not have the computational resources required to build and maintain large neural nets. SVMs evolved from the sound theory to the implementation and experiments, while the NNs followed more heuristic path, from applications and extensive experimentation to the theory. Because now all we can talk about is stacking Restricted Boltzmann Machines and training massive Convolutional Neural Nets. My entire dissertation involved how to utilize Random Forests and weak feature representations to outperform heavily engineered state-of-the-art approaches, fixated on single datasets. I think it’s natural, and even human to a degree, to be biased towards something you have painstakingly studied for a significant chunk of your life. You leave your model to train, cross-validate, and grid search parameters for over a week (and maybe longer, depending on how large your net is and the computational resources at your disposal) just to have your accuracy increase by a tenth of a percent on ImageNet. Otherwise, we just a bunch of machine learning engineers, blindly performing black box learning and operating a set of R, MATLAB, and Python libraries. It’s a methodology with a rational thought process that is entirely dependent on the problem we are trying to solve.
Uncover exclusive techniques that I don't publish on this blog and start building image search engines of your own! I think hype is necessary in figuring out just how well deep learning or other methods can perform in general. Eventually (sometimes never) people figure out how to counter that technique and it ends up being decent but you end up learning a lot after everything is said and done. First, we generally don’t publish papers with negative results that can bring out the bad parts of an algorithm.

As far as industry following academia, I don’t entirely agree with that statement, but it definitely has merit.
My intuition tells me it would, especially on a large dataset such as ImageNet where Convolutional Neural Nets dramatically outperform SVMs. You would just tweak the adversarial image to maximize the loss of the SVM rather than the net.
Now it’s deep learning, and when the dust will begin to settle, we will see people working on combining the lessons learned from deep learning with new stuff. The source of the rant, I think, is that deep learning is vogue for what – the third time since the perceptron?
We as humans don’t (consciously) swap parts of the brain to use the ones that fit a problem better.
However, I want to combine the 2D image with the 3D features (surface Normals) from the Point Cloud but I cannot find a network that can handle this. However, 3D is not my area of expertise so I don’t keep up with that sub-field as well as I do for others. This cluster will celebrate multi-disciplinary approaches to artistic practices and arts-based research across campus and within local Hamilton communities. For those who do not already know how to swim, it allows them to learn simple techniques that will help keep them safe when they encounter any body of water, and for those who do know how to swim, it’s a good refresher during the cooler months that limit swimming outdoors. By spending a little bit more time thinking about the actual problem rather than blindly throwing a bunch of algorithms at the wall and seeing what sticks, I believe that we can only further the research. Hype makes people try deep learning on things most people wouldn’t normally think are a deep learning application.
Secondly, industry mostly follows academia so if they think deep learning is the answer to all problems, they will focusing on it leading to discontent. Many classification pipelines these years focus on combining deep learning features with SVM. I have read several papers using Hybrid classification models like Optimization models (G.A) with Learning models (ANN).
There is no general classifier that can do it all with sufficient quality – no silver bullet.
And they are far from perfect — this is especially true when we migrate our algorithms from academia to industry. We need to sit down, explore the feature space (both empirically and in terms of real-world implications), and then consider our best mode of action. This allows a method to be pushed to its limits which was done with many methods previous like random forests which you mentioned. And more importantly, I see more and more people applying deep learning to problems where simple classifier methods would work just as well. I would suggest giving it a read and following its references, as well as looking at who has cited it recently.
It is nauseating and what is worse is that people use these techniques and really really *DO NOT UNDERSTAND* them.
People seem to be armed with the hammer of deep learning, and now every problem looks like a nail. If I have to listen to another talk from a researcher using these techniques who don’t really understand probability theory or statistics (probably never taking a formal class or even independent, thorough study of either), I will lose my mind. The issue is that when we have a big powerful tool, we want to treat it like a hammer, and that everything else is a nail.

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