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:  Alternatives to Pooling 
1. Abstract of Cited Paper (BIB001):  A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification algorithm is computed with a PCA model selection on scattering vectors. State of the art results are obtained for handwritten digit recognition and texture classification.

Literature Review:
Alternatively, one can also use explicit knowledge of the expected invariants expressed mathematically to define transformations that are robust to a known family of input deformations, using the so-called scattering operators BIB001 , BIB002 , which can be computed in a way interestingly analogous to deep convolutional networks and wavelets. Like convolutional networks, the scattering operators alternate two types of operations: convolution and pooling (as a norm). Unlike convolutional networks, the proposed approach keeps at each level all of the information about the input (in a way that can be inverted) and automatically yields a very sparse (but very high dimensional) representation. Another difference is that the filters are not learned but instead are set so as to guarantee that a priori specified invariances are robustly achieved. Just a few levels were sufficient to achieve impressive results on several benchmark datasets.