49,023 research outputs found
Unsupervised Deep Hashing for Large-scale Visual Search
Learning based hashing plays a pivotal role in large-scale visual search.
However, most existing hashing algorithms tend to learn shallow models that do
not seek representative binary codes. In this paper, we propose a novel hashing
approach based on unsupervised deep learning to hierarchically transform
features into hash codes. Within the heterogeneous deep hashing framework, the
autoencoder layers with specific constraints are considered to model the
nonlinear mapping between features and binary codes. Then, a Restricted
Boltzmann Machine (RBM) layer with constraints is utilized to reduce the
dimension in the hamming space. Extensive experiments on the problem of visual
search demonstrate the competitiveness of our proposed approach compared to
state-of-the-art
Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System
In this paper, we consider an intelligent reflecting surface (IRS)-aided
cell-free massive multiple-input multiple-output system, where the beamforming
at access points and the phase shifts at IRSs are jointly optimized to maximize
energy efficiency (EE). To solve EE maximization problem, we propose an
iterative optimization algorithm by using quadratic transform and Lagrangian
dual transform to find the optimum beamforming and phase shifts. However, the
proposed algorithm suffers from high computational complexity, which hinders
its application in some practical scenarios. Responding to this, we further
propose a deep learning based approach for joint beamforming and phase shifts
design. Specifically, a two-stage deep neural network is trained offline using
the unsupervised learning manner, which is then deployed online for the
predictions of beamforming and phase shifts. Simulation results show that
compared with the iterative optimization algorithm and the genetic algorithm,
the unsupervised learning based approach has higher EE performance and lower
running time.Comment: 6 pages, 4 figure
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
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