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How to Train Your Neural Net
Deep learning for various tasks in the domains of Computer Vision, Natural Language Processing, Time Series Forecasting using PyTorch 1.0+.
pytorch python deep-learning computer-vision
The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
object-detection salient-object-detection image-segmentation unet
Self-Supervised Scene De-occlusion
We investigate the problem of scene de-occlusion, which aims to recover the underlying occlusion ordering and complete the invisible parts of occluded ...
self-supervised-learning computer-vision de-occlusion image-generation
NeuralCook — Image2Ingredients and Cooking Recommendation
Deep learning application to identify ingredients from cooking dishes images and recommend dishes to cook, given a set of ingredients.
cooking text-generation recommendation-systems joint-embeddings
TransMoMo: Invariance-Driven Unsupervised Motion Retargeting
A lightweight video motion retargeting approach that is capable of transferring motion of a person in a source video realistically to another video of a ...
motion-generation video retargeting transmomo
Differentiable Reasoning over Text
We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB).
question-answering multi-hop reasoning entity-linking
Synthesizer: Rethinking Self-Attention in Transformer Models
The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required?
synthesizers transformers attention natural-language-processing
GANSpace: Discovering Interpretable GAN Controls
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis.
generative-adversarial-networks image-generation interpretability interpretable-gans
A Commit History of BERT and its Forks
What a commit history of version-controlled research papers could look like?
natural-language-processing research tutorial
Deep Learning With Graph-Structured Representations
Novel approaches based on the theme of structuring the representations and computations of neural network-based models in the form of a graph.
graph-neural-networks graph-convolutional-networks graph-auto-encoders relational-graph-convolutional-networks
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