Trending Research
Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models
In RDMs, a set of nearest neighbors is retrieved from an external database during training for each training instance, and the diffusion model is conditioned on these informative samples.
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
nvidiagameworks/kaolin-wisp
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Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations.
Collaborative Neural Rendering using Anime Character Sheets
Drawing images of characters with desired poses is an essential but laborious task in anime production.
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #162 on
Image Classification
on ImageNet
Multi-scale Multi-band DenseNets for Audio Source Separation
Anjok07/ultimatevocalremovergui
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This paper deals with the problem of audio source separation.
Musika! Fast Infinite Waveform Music Generation
We release the source code and pretrained autoencoder weights at github. com/marcoppasini/musika, such that a GAN can be trained on a new music domain with a single GPU in a matter of hours.
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints.
Delving into Effective Gradient Matching for Dataset Condensation
In this work, we delve into the gradient matching method from a comprehensive perspective and answer the critical questions of what, how, and where to match.
DeepNet: Scaling Transformers to 1,000 Layers
In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers.
