254,403 research outputs found
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis
Automatic analysis of scanned historical documents comprises a wide range of
image analysis tasks, which are often challenging for machine learning due to a
lack of human-annotated learning samples. With the advent of deep neural
networks, a promising way to cope with the lack of training data is to
pre-train models on images from a different domain and then fine-tune them on
historical documents. In the current research, a typical example of such
cross-domain transfer learning is the use of neural networks that have been
pre-trained on the ImageNet database for object recognition. It remains a
mostly open question whether or not this pre-training helps to analyse
historical documents, which have fundamentally different image properties when
compared with ImageNet. In this paper, we present a comprehensive empirical
survey on the effect of ImageNet pre-training for diverse historical document
analysis tasks, including character recognition, style classification,
manuscript dating, semantic segmentation, and content-based retrieval. While we
obtain mixed results for semantic segmentation at pixel-level, we observe a
clear trend across different network architectures that ImageNet pre-training
has a positive effect on classification as well as content-based retrieval
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Deep neural networks are widely used for classification. These deep models
often suffer from a lack of interpretability -- they are particularly difficult
to understand because of their non-linear nature. As a result, neural networks
are often treated as "black box" models, and in the past, have been trained
purely to optimize the accuracy of predictions. In this work, we create a novel
network architecture for deep learning that naturally explains its own
reasoning for each prediction. This architecture contains an autoencoder and a
special prototype layer, where each unit of that layer stores a weight vector
that resembles an encoded training input. The encoder of the autoencoder allows
us to do comparisons within the latent space, while the decoder allows us to
visualize the learned prototypes. The training objective has four terms: an
accuracy term, a term that encourages every prototype to be similar to at least
one encoded input, a term that encourages every encoded input to be close to at
least one prototype, and a term that encourages faithful reconstruction by the
autoencoder. The distances computed in the prototype layer are used as part of
the classification process. Since the prototypes are learned during training,
the learned network naturally comes with explanations for each prediction, and
the explanations are loyal to what the network actually computes.Comment: The first two authors contributed equally, 8 pages, accepted in AAAI
201
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
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