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Found 11 papers, 5 papers with code
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Multi Label Text Classification
Multi-Label Text Classification
+1
MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
We then extract the dense text representations from the fine-tuned Transformer.
Multi Label Text Classification
Multi-Label Text Classification
+3
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques.
Improving Text-to-Image Synthesis Using Contrastive Learning
To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.
Ranked #9 on
Text-to-Image Generation
on CUB
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
We show that our Generative MMC (GMMC) can be trained discriminatively, generatively, or jointly for image classification and generation.
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
k-sums: another side of k-means
This optimization procedure converges faster to a better local minimum over k-means and many of its variants.
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series
In order to realize the adaptive selection of detectors and run-time parameters according to the input time series, the outputs of FCN are the inputs of two sub-networks: the detector selection network and the run-time parameters selection network.
EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search
In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.
Visualizing and Understanding Deep Neural Networks in CTR Prediction
Recently, many works have been done on visualizing and analyzing the mechanism of deep neural networks in the areas of image processing and natural language processing.
