Search Results for author:
Found 8 papers, 4 papers with code
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
To overcome this limitation, consistency models proposed learning a new class of generative models that directly map noise to data, resulting in a model that can generate an image in as few as one sampling iteration.
Leaf Cultivar Identification via Prototype-enhanced Learning
Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years.
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?
In this paper, we investigate the role of self-supervised representation learning in the context of CDFSL via a thorough evaluation of existing methods.
POP909: A Pop-song Dataset for Music Arrangement Generation
The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files.
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior.
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition
Specifically, the VRE module includes a proxy task which imposes pseudo motion label constraint and temporal coherence constraint on unlabeled videos, while the MRA module could predict the motion information of a static action image by exploiting unlabeled videos.
Inspecting and Interacting with Meaningful Music Representations using VAE
Variational Autoencoders(VAEs) have already achieved great results on image generation and recently made promising progress on music generation.
