2 research outputs found
Measuring Human Perception to Improve Handwritten Document Transcription
The subtleties of human perception, as measured by vision scientists through
the use of psychophysics, are important clues to the internal workings of
visual recognition. For instance, measured reaction time can indicate whether a
visual stimulus is easy for a subject to recognize, or whether it is hard. In
this paper, we consider how to incorporate psychophysical measurements of
visual perception into the loss function of a deep neural network being trained
for a recognition task, under the assumption that such information can enforce
consistency with human behavior. As a case study to assess the viability of
this approach, we look at the problem of handwritten document transcription.
While good progress has been made towards automatically transcribing modern
handwriting, significant challenges remain in transcribing historical
documents. Here we describe a general enhancement strategy, underpinned by the
new loss formulation, which can be applied to the training regime of any deep
learning-based document transcription system. Through experimentation, reliable
performance improvement is demonstrated for the standard IAM and RIMES datasets
for three different network architectures. Further, we go on to show
feasibility for our approach on a new dataset of digitized Latin manuscripts,
originally produced by scribes in the Cloister of St. Gall in the the 9th
century
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
With the increasing artificial intelligence application, deep neural network
(DNN) has become an emerging task. However, to train a good deep learning model
will suffer from enormous computation cost and energy consumption. Recently,
blockchain has been widely used, and during its operation, a huge amount of
computation resources are wasted for the Proof of Work (PoW) consensus. In this
paper, we propose DLBC to exploit the computation power of miners for deep
learning training as proof of useful work instead of calculating hash values.
it distinguishes itself from recent proof of useful work mechanisms by
addressing various limitations of them. Specifically, DLBC handles multiple
tasks, larger model and training datasets, and introduces a comprehensive
ranking mechanism that considers tasks difficulty(e.g., model complexity,
network burden, data size, queue length). We also applied DNN-watermark [1] to
improve the robustness. In Section V, the average overhead of digital signature
is 1.25, 0.001, 0.002 and 0.98 seconds, respectively, and the average overhead
of network is 3.77, 3.01, 0.37 and 0.41 seconds, respectively. Embedding a
watermark takes 3 epochs and removing a watermark takes 30 epochs. This penalty
of removing watermark will prevent attackers from stealing, improving, and
resubmitting DL models from honest miners