Trending Research
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images.
Physics-based Deep Learning
This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
Probabilistic Forecasting with Temporal Convolutional Neural Network
unit8co/darts
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We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
TabNet: Attentive Interpretable Tabular Learning
We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet.
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Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
microsoft/qlib
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In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image.
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT.
Resolution-robust Large Mask Inpainting with Fourier Convolutions
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
LibFewShot: A Comprehensive Library for Few-shot Learning
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks.
