DL:深度学习算法(神经网络模型集合)概览之《THE NEURAL NETWORK ZOO》的中文解释和感悟(二) 相关文章 AE Autoencoders (AE) are somewhat similar to FFNNs as AEs are more like a different use of FFNNs than a fundamentally different architecture. The basic idea behind autoencoders is to encode information (as in compress, not encrypt) automatically, hence the name. The entire network always resembles an hourglass like shape, with smaller hidden layers than the input and output layers. AEs are also always symmetrical around the middle layer(s) (one or two depending on an even or odd amount of layers). The smallest layer(s) is|are almost always in the middle, the place where the information is most compressed (the chokepoint of the network). Everything up to the middle is called the encoding part, everything after the middle the decoding and the middle (surprise) the code. One can train them using backpropagation by feeding input and setting the error to be the difference between the input and what came out. AEs can be built symmetrically when it comes to weights as well, so the encoding weights are the same as the decoding weights. Bourlard, Hervé, and Yves Kamp. “Auto-association by multilayer perceptrons and singular value decomposition.” Biological cybernetics 59.4-5 (1988): 291-294. VAE Variational autoencoders (VAE) have the same architecture as AEs but are “taught” something else: an approximated probability distribution of the input samples. It’s a bit back to the roots as they are bit more closely related to BMs and RBMs. They do however rely on Bayesian mathematics regarding probabilistic inference and independence, as well as a re-parametrisation trick to achieve this different representation. The inference and independence parts make sense intuitively, but they rely on somewhat complex mathematics. The basics come down to this: take influence into account. If one thing happens in one place and something else happens somewhere else, they are not necessarily related. If they are not related, then the error propagation should consider that. This is a useful approach because neural networks are large graphs (in a way), so it helps if you can rule out influence from some nodes to other nodes as you dive into deeper layers. Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013). DAE Denoising autoencoders (DAE) are AEs where we don’t feed just the input data, but we feed the input data with noise (like making an image more grainy). We compute the error the same way though, so the output of the network is compared to the original input without noise. This encourages the network not to learn details but broader features, as learning smaller features often turns out to be “wrong” due to it constantly changing with noise. Vincent, Pascal, et al. “Extracting and composing robust features with denoising autoencoders.” Proceedings of the 25th international conference on Machine learning. ACM, 2008. SAE Sparse autoencoders (SAE) are in a way the opposite of AEs. Instead of teaching a network to represent a bunch of information in less “space” or nodes, we try to encode information in more space. So instead of the network converging in the middle and then expanding back to the input size, we blow up the middle. These types of networks can be used to extract many small features from a dataset. If one were to train a SAE the same way as an AE, you would in almost all cases end up with a pretty useless identity network (as in what comes in is what comes out, without any transformation or decomposition). To prevent this, instead of feeding back the input, we feed back the input plus a sparsity driver. This sparsity driver can take the form of a threshold filter, where only a certain error is passed back and trained, the other error will be “irrelevant” for that pass and set to zero. In a way this resembles spiking neural networks, where not all neurons fire all the time (and points are scored for biological plausibility). Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann LeCun. “Efficient learning of sparse representations with an energy-based model.” Proceedings of NIPS. 2007. |
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