20,394 research outputs found
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
TRANSLATION AND CROSS CULTURAL UNDERSTANDING (CCU)
Translation and Cross Cultural Understanding (CCU) are two compulsory subjects given in the EnglishDepartment. The two courses are closely related to each other since both skills may improve the students’
language competence, especially those who want to be a professional translator. Mastering the sourcelanguage (S-L), i.e. a foreign language (English), the target language (T-L), e.g. Indonesian, and masteringthe text materials to be translated will not make a good translator if we do not have enough practice andexperience. The paper aims to elaborate some concepts, techniques of translation and those of crosscultural understanding and to discuss some problems in translation practice and cross cultural
understanding
個人が用いる単語の意味のモデル化とその応用
学位の種別: 修士University of Tokyo(東京大学
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
We propose a learning problem involving adapting a pre-trained source model
to the target domain for classifying all classes that appeared in the source
data, using target data that covers only a partial label space. This problem is
practical, as it is unrealistic for the target end-users to collect data for
all classes prior to adaptation. However, it has received limited attention in
the literature. To shed light on this issue, we construct benchmark datasets
and conduct extensive experiments to uncover the inherent challenges. We found
a dilemma -- on the one hand, adapting to the new target domain is important to
claim better performance; on the other hand, we observe that preserving the
classification accuracy of classes missing in the target adaptation data is
highly challenging, let alone improving them. To tackle this, we identify two
key directions: 1) disentangling domain gradients from classification
gradients, and 2) preserving class relationships. We present several effective
solutions that maintain the accuracy of the missing classes and enhance the
overall performance, establishing solid baselines for holistic transfer of
pre-trained models with partial target data.Comment: Accepted to NeurIPS 2023 main trac
- …