14,919 research outputs found
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
MCE 2018: The 1st Multi-target Speaker Detection and Identification Challenge Evaluation
The Multi-target Challenge aims to assess how well current speech technology
is able to determine whether or not a recorded utterance was spoken by one of a
large number of blacklisted speakers. It is a form of multi-target speaker
detection based on real-world telephone conversations. Data recordings are
generated from call center customer-agent conversations. The task is to measure
how accurately one can detect 1) whether a test recording is spoken by a
blacklisted speaker, and 2) which specific blacklisted speaker was talking.
This paper outlines the challenge and provides its baselines, results, and
discussions.Comment: http://mce.csail.mit.edu . arXiv admin note: text overlap with
arXiv:1807.0666
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
We draw a formal connection between using synthetic training data to optimize
neural network parameters and approximate, Bayesian, model-based reasoning. In
particular, training a neural network using synthetic data can be viewed as
learning a proposal distribution generator for approximate inference in the
synthetic-data generative model. We demonstrate this connection in a
recognition task where we develop a novel Captcha-breaking architecture and
train it using synthetic data, demonstrating both state-of-the-art performance
and a way of computing task-specific posterior uncertainty. Using a neural
network trained this way, we also demonstrate successful breaking of real-world
Captchas currently used by Facebook and Wikipedia. Reasoning from these
empirical results and drawing connections with Bayesian modeling, we discuss
the robustness of synthetic data results and suggest important considerations
for ensuring good neural network generalization when training with synthetic
data.Comment: 8 pages, 4 figure
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
In this work we present a framework for the recognition of natural scene
text. Our framework does not require any human-labelled data, and performs word
recognition on the whole image holistically, departing from the character based
recognition systems of the past. The deep neural network models at the centre
of this framework are trained solely on data produced by a synthetic text
generation engine -- synthetic data that is highly realistic and sufficient to
replace real data, giving us infinite amounts of training data. This excess of
data exposes new possibilities for word recognition models, and here we
consider three models, each one "reading" words in a different way: via 90k-way
dictionary encoding, character sequence encoding, and bag-of-N-grams encoding.
In the scenarios of language based and completely unconstrained text
recognition we greatly improve upon state-of-the-art performance on standard
datasets, using our fast, simple machinery and requiring zero data-acquisition
costs
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