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Found 8 papers, 4 papers with code
Cross Encoding as Augmentation: Towards Effective Educational Text Classification
To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification.
Paperswithtopic: Topic Identification from Paper Title Only
The deep learning field is growing rapidly as witnessed by the exponential growth of papers submitted to journals, conferences, and pre-print servers.
Predicting decision-making in the future: Human versus Machine
Deep neural networks (DNNs) have become remarkably successful in data prediction, and have even been used to predict future actions based on limited input.
Visualizing the embedding space to explain the effect of knowledge distillation
Recent research has found that knowledge distillation can be effective in reducing the size of a network and in increasing generalization.
Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation
In this work, we compared the recognition performance and attention patterns of humans and machines during a two-alternative forced-choice FER task.
Facial Expression Recognition
Facial Expression Recognition (FER)
Label quality in AffectNet: results of crowd-based re-annotation
AffectNet is one of the most popular resources for facial expression recognition (FER) on relatively unconstrained in-the-wild images.
Facial Expression Recognition
Facial Expression Recognition (FER)
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training.
3FabRec: Fast Few-shot Face alignment by Reconstruction
Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters.
Ranked #4 on
Face Alignment
on AFLW-19
(NME_box (%, Full) metric, using extra
training data)
