You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Representation Learning: A Review and New Perspectives 
Chapter Title:  PSD 
1. Abstract of Cited Paper (BIB001):  Prior work has shown that features which appear to be biologically plausible as well as empirically useful can be found by sparse coding with a prior such as a laplacian (L1) that promotes sparsity. We show how smoother priors can preserve the benefits of these sparse priors while adding stability to the Maximum A-Posteriori (MAP) estimate that makes it more useful for prediction problems. Additionally, we show how to calculate the derivative of the MAP estimate efficiently with implicit differentiation. One prior that can be differentiated this way is KL-regularization. We demonstrate its effectiveness on a wide variety of applications, and find that online optimization of the parameters of the KL-regularized model can significantly improve prediction performance. 
2. Abstract of Cited Paper (BIB002):  Several recently-proposed architectures for high-performance object recognition are composed of two main stages: a feature extraction stage that extracts locally-invariant feature vectors from regularly spaced image patches, and a somewhat generic supervised classifier. The first stage is often composed of three main modules: (1) a bank of filters (often oriented edge detectors); (2) a non-linear transform, such as a point-wise squashing functions, quantization, or normalization; (3) a spatial pooling operation which combines the outputs of similar filters over neighboring regions. We propose a method that automatically learns such feature extractors in an unsupervised fashion by simultaneously learning the filters and the pooling units that combine multiple filter outputs together. The method automatically generates topographic maps of similar filters that extract features of orientations, scales, and positions. These similar filters are pooled together, producing locally-invariant outputs. The learned feature descriptors give comparable results as SIFT on image recognition tasks for which SIFT is well suited, and better results than SIFT on tasks for which SIFT is less well suited. 
3. Abstract of Cited Paper (BIB003):  In many recent object recognition systems, feature extraction stages are generally composed of a filter bank, a non-linear transformation, and some sort of feature pooling layer. Most systems use only one stage of feature extraction in which the filters are hard-wired, or two stages where the filters in one or both stages are learned in supervised or unsupervised mode. This paper addresses three questions: 1. How does the non-linearities that follow the filter banks influence the recognition accuracy? 2. does learning the filter banks in an unsupervised or supervised manner improve the performance over random filters or hardwired filters? 3. Is there any advantage to using an architecture with two stages of feature extraction, rather than one? We show that using non-linearities that include rectification and local contrast normalization is the single most important ingredient for good accuracy on object recognition benchmarks. We show that two stages of feature extraction yield better accuracy than one. Most surprisingly, we show that a two-stage system with random filters can yield almost 63% recognition rate on Caltech-101, provided that the proper non-linearities and pooling layers are used. Finally, we show that with supervised refinement, the system achieves state-of-the-art performance on NORB dataset (5.6%) and unsupervised pre-training followed by supervised refinement produces good accuracy on Caltech-101 (≫ 65%), and the lowest known error rate on the undistorted, unprocessed MNIST dataset (0.53%). 
4. Abstract of Cited Paper (BIB004):  Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks. 
5. Abstract of Cited Paper (BIB005):  We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features. While sparse coding has become an increasingly popular method for learning visual features, it is most often trained at the patch level. Applying the resulting filters convolutionally results in highly redundant codes because overlapping patches are encoded in isolation. By training convolutionally over large image windows, our method reduces the redudancy between feature vectors at neighboring locations and improves the efficiency of the overall representation. In addition to a linear decoder that reconstructs the image from sparse features, our method trains an efficient feed-forward encoder that predicts quasi-sparse features from the input. While patch-based training rarely produces anything but oriented edge detectors, we show that convolutional training produces highly diverse filters, including center-surround filters, corner detectors, cross detectors, and oriented grating detectors. We show that using these filters in multistage convolutional network architecture improves performance on a number of visual recognition and detection tasks. 
6. Abstract of Cited Paper (BIB006):  In this work we present a system to automatically learn features from audio in an unsupervised manner. Our method first learns an overcomplete dictionary which can be used to sparsely decompose log-scaled spectrograms. It then trains an efficient encoder which quickly maps new inputs to approximations of their sparse representations using the learned dictionary. This avoids expensive iterative procedures usually required to infer sparse codes. We then use these sparse codes as inputs for a linear Support Vector Machine (SVM). Our system achieves 83.4% accuracy in predicting genres on the GTZAN dataset, which is competitive with current state-of-the-art approaches. Furthermore, the use of a simple linear classifier combined with a fast feature extraction system allows our approach to scale well to large datasets.

Literature Review:
Sparse coding BIB001 may be viewed as a kind of autoencoder that uses a linear decoder with a squared reconstruction error, but whose nonparametric encoder f performs the comparatively nontrivial and relatively costly iterative minimization of (2). A practically successful variant of sparse coding and autoencoders, named PSD BIB005 , replaces that costly and highly nonlinear encoding step by a fast noniterative approximation during recognition (computing the learned features). PSD has been applied to object recognition in images and video BIB003 , BIB006 , BIB004 , but also to audio BIB007 , mostly within the framework of multistage convolutional deep architectures (Section 11.2). The main idea can be summarized by the following equation for the training criterion, which is simultaneously optimized with respect to hidden codes (representation) h ðtÞ and with respect to parameters ðW ; Þ: ð26Þ where x ðtÞ is the input vector for example t, h ðtÞ is the optimized hidden code for that example, and f ðÁÞ is the encoding function, the simplest variant being where encoding weights are the transpose of decoding weights. Many variants have been proposed, including the use of a shrinkage operation instead of the hyperbolic tangent BIB006 . Note that the L1 penalty on h tends to make them sparse, and how this is the same criterion as sparse coding with dictionary learning (3) except for the additional constraint that one should be able to approximate the sparse codes h with a parameterized encoder f ðxÞ. One can thus view PSD as an approximation to sparse coding, where we obtain a fast approximate encoder. Once PSD is trained, object representations f ðxÞ are used to feed a classifier. They are computed quickly and can be further fine tuned: The encoder can be viewed as one stage or one layer of a trainable multistage system such as a feedforward neural network. PSD can also be seen as a kind of autoencoder, where the codes h are given some freedom that can help to further improve reconstruction. One can also view the encoding penalty added on top of sparse coding as a kind of regularizer that forces the sparse codes to be nearly computable by a smooth and efficient encoder. This is in contrast with the codes obtained by complete optimization of the sparse coding criterion, which are highly nonsmooth or even nondifferentiable, a problem that motivated other approaches to smooth the inferred codes of sparse coding BIB002 , so a sparse coding stage could be jointly optimized along with following stages of a deep architecture.