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Found 6 papers, 1 papers with code
DODA: Diffusion for Object-detection Domain Adaptation in Agriculture
The diverse and high-quality content generated by recent generative models demonstrates the great potential of using synthetic data to train downstream models.
Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version.
Multi-Adversarial Learning for Cross-Lingual Word Embeddings
Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings -- maps of matching words across languages -- without supervision.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
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Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings
Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but also across different languages.
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later
This paper describes the University of Geneva{'}s submission to the CoNLL 2017 shared task Multilingual Parsing from Raw Text to Universal Dependencies (listed as the CLCL (Geneva) entry).
Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings
Traditional machine translation evaluation metrics such as BLEU and WER have been widely used, but these metrics have poor correlations with human judgements because they badly represent word similarity and impose strict identity matching.
