103 research outputs found
Motion Invariance in Visual Environments
The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams, just as
happens in nature. In this paper, we claim that their processing naturally
leads to formulate the motion invariance principle, which enables the
construction of a new theory of visual learning based on convolutional
features. The theory addresses a number of intriguing questions that arise in
natural vision, and offers a well-posed computational scheme for the discovery
of convolutional filters over the retina. They are driven by the Euler-Lagrange
differential equations derived from the principle of least cognitive action,
that parallels laws of mechanics. Unlike traditional convolutional networks,
which need massive supervision, the proposed theory offers a truly new scenario
in which feature learning takes place by unsupervised processing of video
signals. An experimental report of the theory is presented where we show that
features extracted under motion invariance yield an improvement that can be
assessed by measuring information-based indexes.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0711
Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
Human visual attention is a complex phenomenon. A computational modeling of
this phenomenon must take into account where people look in order to evaluate
which are the salient locations (spatial distribution of the fixations), when
they look in those locations to understand the temporal development of the
exploration (temporal order of the fixations), and how they move from one
location to another with respect to the dynamics of the scene and the mechanics
of the eyes (dynamics). State-of-the-art models focus on learning saliency maps
from human data, a process that only takes into account the spatial component
of the phenomenon and ignore its temporal and dynamical counterparts. In this
work we focus on the evaluation methodology of models of human visual
attention. We underline the limits of the current metrics for saliency
prediction and scanpath similarity, and we introduce a statistical measure for
the evaluation of the dynamics of the simulated eye movements. While deep
learning models achieve astonishing performance in saliency prediction, our
analysis shows their limitations in capturing the dynamics of the process. We
find that unsupervised gravitational models, despite of their simplicity,
outperform all competitors. Finally, exploiting a crowd-sourcing platform, we
present a study aimed at evaluating how strongly the scanpaths generated with
the unsupervised gravitational models appear plausible to naive and expert
human observers
The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns
Artificial intelligence is continuously seeking novel challenges and
benchmarks to effectively measure performance and to advance the
state-of-the-art. In this paper we introduce KANDY, a benchmarking framework
that can be used to generate a variety of learning and reasoning tasks inspired
by Kandinsky patterns. By creating curricula of binary classification tasks
with increasing complexity and with sparse supervisions, KANDY can be used to
implement benchmarks for continual and semi-supervised learning, with a
specific focus on symbol compositionality. Classification rules are also
provided in the ground truth to enable analysis of interpretable solutions.
Together with the benchmark generation pipeline, we release two curricula, an
easier and a harder one, that we propose as new challenges for the research
community. With a thorough experimental evaluation, we show how both
state-of-the-art neural models and purely symbolic approaches struggle with
solving most of the tasks, thus calling for the application of advanced
neuro-symbolic methods trained over time
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier
In the last decade, motivated by the success of Deep Learning, the scientific
community proposed several approaches to make the learning procedure of Neural
Networks more effective. When focussing on the way in which the training data
are provided to the learning machine, we can distinguish between the classic
random selection of stochastic gradient-based optimization and more involved
techniques that devise curricula to organize data, and progressively increase
the complexity of the training set. In this paper, we propose a novel training
procedure named Friendly Training that, differently from the aforementioned
approaches, involves altering the training examples in order to help the model
to better fulfil its learning criterion. The model is allowed to simplify those
examples that are too hard to be classified at a certain stage of the training
procedure. The data transformation is controlled by a developmental plan that
progressively reduces its impact during training, until it completely vanishes.
In a sense, this is the opposite of what is commonly done in order to increase
robustness against adversarial examples, i.e., Adversarial Training.
Experiments on multiple datasets are provided, showing that Friendly Training
yields improvements with respect to informed data sub-selection routines and
random selection, especially in deep convolutional architectures. Results
suggest that adapting the input data is a feasible way to stabilize learning
and improve the generalization skills of the network.Comment: 9 pages, 5 figure
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientifc community developed strategies to order the examples
according to their estimated complexity, to distil knowledge
from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently
proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process
of a neural classifer. The transformation progressively fadesout as long as training proceeds, until it completely vanishes.
In this work we revisit and extend this idea, introducing a
radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial
Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make
them easier to be handled by the classifer at the current stage
of the training procedure. The auxiliary network is trained
jointly with the neural classifer, thus intrinsically increasing
the “depth” of the classifer, and it is expected to spot general regularities in the data alteration process. The effect of
the auxiliary network is progressively reduced up to the end
of training, when it is fully dropped and the classifer is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure
involving several datasets and different neural architectures
shows that Neural Friendly Training overcomes the originally
proposed Friendly Training technique, improving the generalization of the classifer, especially in the case of noisy data
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