11,370 research outputs found
Face Identification and Clustering
In this thesis, we study two problems based on clustering algorithms. In the
first problem, we study the role of visual attributes using an agglomerative
clustering algorithm to whittle down the search area where the number of
classes is high to improve the performance of clustering. We observe that as we
add more attributes, the clustering performance increases overall. In the
second problem, we study the role of clustering in aggregating templates in a
1:N open set protocol using multi-shot video as a probe. We observe that by
increasing the number of clusters, the performance increases with respect to
the baseline and reaches a peak, after which increasing the number of clusters
causes the performance to degrade. Experiments are conducted using recently
introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition
datasets
Model Complexity-Accuracy Trade-off for a Convolutional Neural Network
Convolutional Neural Networks(CNN) has had a great success in the recent
past, because of the advent of faster GPUs and memory access. CNNs are really
powerful as they learn the features from data in layers such that they exhibit
the structure of the V-1 features of the human brain. A huge bottleneck, in
this case, is that CNNs are very large and have a very high memory footprint,
and hence they cannot be employed on devices with limited storage such as
mobile phone, IoT etc. In this work, we study the model complexity versus
accuracy trade-off on MNSIT dataset, and give a concrete framework for handling
such a problem, given the worst case accuracy that a system can tolerate. In
our work, we reduce the model complexity by 236 times, and memory footprint by
19.5 times compared to the base model while achieving worst case accuracy
threshold
Mixing layer instability and vorticity amplification in a creeping viscoelastic flow
We report quantitative evidence of mixing-layer elastic instability in a
viscoelastic fluid flow between two widely spaced obstacles hindering a channel
flow at and . Two mixing layers with nonuniform shear velocity
profiles are formed in the region between the obstacles. The mixing-layer
instability arises in the vicinity of an inflection point on the shear velocity
profile with a steep variation in the elastic stress. The instability results
in an intermittent appearance of small vortices in the mixing layers and an
amplification of spatio-temporal averaged vorticity in the elastic turbulence
regime. The latter is characterized through scaling of friction factor with
, and both pressure and velocity spectra. Furthermore, the observations
reported provide improved understanding of the stability of the mixing layer in
a viscoelastic fluid at large elasticity, i.e. and , and
oppose the current view of suppression of vorticity solely by polymer
additives.Comment: 6 pages, 7 figure
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