Introduces animal2vec, a self-supervised transformer for sparse bioacoustic audio, and the MeerKAT meerkat vocalization dataset, claiming outperformance over baselines including in few-shot settings.
AttentionViz: A global view of transformer attention
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abstract
Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allows these models to learn rich, contextual relationships between elements of a sequence. The main idea behind our method is to visualize a joint embedding of the query and key vectors used by transformer models to compute attention. Unlike previous attention visualization techniques, our approach enables the analysis of global patterns across multiple input sequences. We create an interactive visualization tool, AttentionViz (demo: http://attentionviz.com), based on these joint query-key embeddings, and use it to study attention mechanisms in both language and vision transformers. We demonstrate the utility of our approach in improving model understanding and offering new insights about query-key interactions through several application scenarios and expert feedback.
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A GAN with elliptical-spiral feature mixing, star-operation blocks, and Sobel edge loss produces more realistic synthetic handwriting and lowers HTR error rates on English and Vietnamese datasets.
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animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics
Introduces animal2vec, a self-supervised transformer for sparse bioacoustic audio, and the MeerKAT meerkat vocalization dataset, claiming outperformance over baselines including in few-shot settings.
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SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation
A GAN with elliptical-spiral feature mixing, star-operation blocks, and Sobel edge loss produces more realistic synthetic handwriting and lowers HTR error rates on English and Vietnamese datasets.