Search Results for author:
Found 10 papers, 7 papers with code
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Inspired by recent studies on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models.
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity.
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
Text data augmentation is a complex problem due to the discrete nature of sentences.
User Guide for KOTE: Korean Online Comments Emotions Dataset
The emotion taxonomy of the 43 emotions is systematically established by cluster analysis of Korean emotion concepts expressed on word embedding space.
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
The global warpage implies that the latent space is not well-aligned globally and therefore the global traversal directions are bound to show limited success on it.
GATSBI: Generative Agent-centric Spatio-temporal Object Interaction
We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatio-temporal context of the agent's actions.
MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks
Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models.
Ranked #1 on
Single Image Deraining
on RainCityscapes
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS
However, the computation of the divergence term in SURE is difficult to implement in a neural network framework, and the condition to avoid trivial identity mapping is not well defined.
Boosting CNN beyond Label in Inverse Problems
In this paper, we show that the recent unsupervised learning methods such as Noise2Noise, Stein's unbiased risk estimator (SURE)-based denoiser, and Noise2Void are closely related to each other in their formulation of an unbiased estimator of the prediction error, but each of them are associated with its own limitations.
Polarization Fraction Measurement in same sign WW scattering using Deep Learning
Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics.
High Energy Physics - Phenomenology High Energy Physics - Experiment
