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Found 8 papers, 7 papers with code
SA-GS: Scale-Adaptive Gaussian Splatting for Training-Free Anti-Aliasing
This integration is actually a limiting case of super-sampling, which significantly improves anti-aliasing performance over vanilla Gaussian Splatting.
AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior
The first component is a piano arranger that generates piano accompaniment for the lead sheet by transferring texture styles to the chords using latent chord-texture disentanglement and heuristic retrieval of texture donors.
Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls
We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations.
AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System
We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody.
AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer
Accompaniment arrangement is a difficult music generation task involving intertwined constraints of melody, harmony, texture, and music structure.
Noise against noise: stochastic label noise helps combat inherent label noise
The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates.
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption. The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN).
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Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping.
