75 research outputs found
Random convolution ensembles
A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective
Incremental multi-domain learning with network latent tensor factorization
The prominence of deep learning, large amount of annotated data and
increasingly powerful hardware made it possible to reach remarkable performance
for supervised classification tasks, in many cases saturating the training
sets. However the resulting models are specialized to a single very specific
task and domain. Adapting the learned classification to new domains is a hard
problem due to at least three reasons: (1) the new domains and the tasks might
be drastically different; (2) there might be very limited amount of annotated
data on the new domain and (3) full training of a new model for each new task
is prohibitive in terms of computation and memory, due to the sheer number of
parameters of deep CNNs. In this paper, we present a method to learn
new-domains and tasks incrementally, building on prior knowledge from already
learned tasks and without catastrophic forgetting. We do so by jointly
parametrizing weights across layers using low-rank Tucker structure. The core
is task agnostic while a set of task specific factors are learnt on each new
domain. We show that leveraging tensor structure enables better performance
than simply using matrix operations. Joint tensor modelling also naturally
leverages correlations across different layers. Compared with previous methods
which have focused on adapting each layer separately, our approach results in
more compact representations for each new task/domain. We apply the proposed
method to the 10 datasets of the Visual Decathlon Challenge and show that our
method offers on average about 7.5x reduction in number of parameters and
competitive performance in terms of both classification accuracy and Decathlon
score.Comment: AAAI2
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Visual recognition algorithms are required today to exhibit adaptive
abilities. Given a deep model trained on a specific, given task, it would be
highly desirable to be able to adapt incrementally to new tasks, preserving
scalability as the number of new tasks increases, while at the same time
avoiding catastrophic forgetting issues. Recent work has shown that masking the
internal weights of a given original conv-net through learned binary variables
is a promising strategy. We build upon this intuition and take into account
more elaborated affine transformations of the convolutional weights that
include learned binary masks. We show that with our generalization it is
possible to achieve significantly higher levels of adaptation to new tasks,
enabling the approach to compete with fine tuning strategies by requiring
slightly more than 1 bit per network parameter per additional task. Experiments
on two popular benchmarks showcase the power of our approach, that achieves the
new state of the art on the Visual Decathlon Challenge
Budget-Aware Adapters for Multi-Domain Learning
Multi-Domain Learning (MDL) refers to the problem of learning a set of models
derived from a common deep architecture, each one specialized to perform a task
in a certain domain (e.g., photos, sketches, paintings). This paper tackles MDL
with a particular interest in obtaining domain-specific models with an
adjustable budget in terms of the number of network parameters and
computational complexity. Our intuition is that, as in real applications the
number of domains and tasks can be very large, an effective MDL approach should
not only focus on accuracy but also on having as few parameters as possible. To
implement this idea we derive specialized deep models for each domain by
adapting a pre-trained architecture but, differently from other methods, we
propose a novel strategy to automatically adjust the computational complexity
of the network. To this aim, we introduce Budget-Aware Adapters that select the
most relevant feature channels to better handle data from a novel domain. Some
constraints on the number of active switches are imposed in order to obtain a
network respecting the desired complexity budget. Experimentally, we show that
our approach leads to recognition accuracy competitive with state-of-the-art
approaches but with much lighter networks both in terms of storage and
computation.Comment: ICCV 201
Keypoints-based background model and foreground pedestrian extraction for future smart cameras
International audienceIn this paper, we present a method for background modeling using only keypoints, and detection of foreground moving pedestrians using background keypoints substraction followed by adaBoost classification of foreground keypoints. A first experimental evaluation shows very promising detection performances in real-time
New Descriptor for Glomerulus Detection in Kidney Microscopy Image
Glomerulus detection is a key step in histopathological evaluation of
microscopy images of kidneys. However, the task of automatic detection of
glomeruli poses challenges due to the disparity in sizes and shapes of
glomeruli in renal sections. Moreover, extensive variations of their
intensities due to heterogeneity in immunohistochemistry staining are also
encountered. Despite being widely recognized as a powerful descriptor for
general object detection, the rectangular histogram of oriented gradients
(Rectangular HOG) suffers from many false positives due to the aforementioned
difficulties in the context of glomerulus detection.
A new descriptor referred to as Segmental HOG is developed to perform a
comprehensive detection of hundreds of glomeruli in images of whole kidney
sections. The new descriptor possesses flexible blocks that can be adaptively
fitted to input images to acquire robustness to deformations of glomeruli.
Moreover, the novel segmentation technique employed herewith generates high
quality segmentation outputs and the algorithm is assured to converge to an
optimal solution. Consequently, experiments using real world image data reveal
that Segmental HOG achieves significant improvements in detection performance
compared to Rectangular HOG.
The proposed descriptor and method for glomeruli detection present promising
results and is expected to be useful in pathological evaluation
- âŠ