Trending Research
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.
Fine-Tuning Language Models from Human Preferences
lvwerra/trl
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Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
Generating Sequences With Recurrent Neural Networks
sjvasquez/handwriting-synthesis
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This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.
Ranked #38 on
Language Modelling
on enwik8
Learning the Beauty in Songs: Neural Singing Voice Beautifier
MoonInTheRiver/DiffSinger
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ACL 2022
Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one.
InstructPix2Pix: Learning to Follow Image Editing Instructions
timothybrooks/instruct-pix2pix
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We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
Text2LIVE: Text-Driven Layered Image and Video Editing
omerbt/Text2LIVE
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Given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e. g., object's texture) or augment the scene with visual effects (e. g., smoke, fire) in a semantically meaningful manner.
SNAKE: Shape-aware Neural 3D Keypoint Field
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection?
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function.
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
microsoft/biogpt
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Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
Ranked #1 on
Question Answering
on PubMedQA
On the Expressive Power of Geometric Graph Neural Networks
chaitjo/geometric-gnn-dojo
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The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test.
