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GeoTTER framework improves zero-shot classification using optimal transport

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GeoTTER, a new framework designed to enhance zero-shot classification by refining optimal transport methods. GeoTTER addresses limitations in conventional approaches by integrating local geometric structure through graph-Laplacian smoothing and correcting coherent angular drift with a multi-objective optimization. This novel framework demonstrates significant improvements, achieving a median increase of 6.82% over standard zero-shot methods and 2.13% over the OTTER method across various benchmarks. AI

IMPACT Enhances zero-shot classification capabilities, potentially improving performance in tasks requiring classification without direct training examples.

RANK_REASON The cluster contains an academic paper detailing a new method for zero-shot classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GeoTTER framework improves zero-shot classification using optimal transport

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The cluster contains an academic paper detailing a new method for zero-shot classification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wei-Yang Alex Lee, Rudrasis Chakraborty, Vishnu Lokhande ·

    GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification

    arXiv:2609.13518v1 Announce Type: new Abstract: We present GeoTTER, a novel framework that redefines optimal transport in the realm of zero-shot classification. Conventional methods often suffer from miscalibration and a lack of adaptability, as they rely on fixed cost matrices d…

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