@proceedings {2618,
	title = {Lecture Notes in Computer ScienceComputer Vision {\textendash} ECCV 2016Ambient Sound Provides Supervision for Visual Learning},
	year = {2016},
	month = {10/2016},
	pages = {801 - 816},
	address = {Cham},
	abstract = {<p>The sound of crashing waves, the roar of fast-moving cars {\textendash} sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound associated with a video frame. We show that, through this process, the network learns a representation that conveys information about objects and scenes. We evaluate this representation on several recognition tasks, finding that its performance is comparable to that of other state-of-the-art unsupervised learning methods. Finally, we show through visualizations that the network learns units that are selective to objects that are often associated with characteristic sounds.</p>
},
	keywords = {convolutional networks, Sound, unsupervised learning},
	isbn = {978-3-319-46447-3},
	issn = {0302-9743},
	doi = {10.1007/978-3-319-46448-010.1007/978-3-319-46448-0_48},
	url = {http://link.springer.com/10.1007/978-3-319-46448-0},
	author = {Owens, Andrew and Isola, P. and Josh H. McDermott and William T. Freeman and Torralba, Antonio}
}
@article {3281,
	title = {Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval},
	journal = {arXiv.org},
	year = {2016},
	month = {03/2016},
	abstract = {<p>The goal of this work is the computation of very compact binary hashes for image instance retrieval. Our approach has two novel contributions. The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks. NIP is able to produce compact and well-performing descriptors with visual representations extracted from convolutional neural networks. We specifically incorporate scale, translation and rotation invariances but the scheme can be extended to any arbitrary sets of transformations. We also show that using moments of increasing order throughout nesting is important. The NIP descriptors are then hashed to the target code size (32-256 bits) with a Restricted Boltzmann Machine with a novel batch-level regularization scheme specifically designed for the purpose of hashing (RBMH). A thorough empirical evaluation with state-of-the-art shows that the results obtained both with the NIP descriptors and the NIP+RBMH hashes are consistently outstanding across a wide range of datasets.</p>
},
	keywords = {CNN, Hashing, Image Instance Retrieval, Invariant Representation, Regularization, unsupervised learning},
	url = {https://arxiv.org/abs/1603.04595},
	author = {Olivier Mor{\`e}re and Antoine Veillard and Vijay Chandrasekhar and Tomaso Poggio}
}
@conference {1141,
	title = {A Deep Representation for Invariance and Music Classification},
	booktitle = {ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing},
	year = {2014},
	month = {05/04/2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Florence, Italy},
	keywords = {acoustic signal processing, signal representation, unsupervised learning},
	doi = {10.1109/ICASSP.2014.6854954},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6854954},
	author = {Chiyuan Zhang and Georgios Evangelopoulos and Stephen Voinea and Lorenzo Rosasco and Tomaso Poggio}
}
