arXiv:2206.04805
공식 코드 · Papers with Code
Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022
원문 초록
We build a classification model for the BirdCLEF 2022 challenge using unsupervised methods. We implement an unsupervised representation of the training dataset using a triplet loss on spectrogram representation of audio motifs. Our best model performs with a score of 0.48 on the public leaderboard.
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Improving Large Language Model Safety with Contrastive Representation Learning
Unsupervised Deep Clustering of MNIST with Triplet-Enhanced Convolutional Autoencoders
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging
Exploring Generalized Gait Recognition: Reducing Redundancy and Noise within Indoor and Outdoor Datasets
Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment
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