miscellaneous-2

Product Categorization

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in product-categorization-2

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Libraries

i

Use these libraries to find product-categorization-2 models and implementations

Datasets

Atlas

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Most implemented papers

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

R. You, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu•Wed Oct 31 2018

This work proposes a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and a shallow and wide probabilistic label tree (PLT), which allow to handle millions of labels, especially for "tail labels".

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Taming Pretrained Transformers for Extreme Multi-label Text Classification

Kai Zhong, Wei-Cheng Chang, Hsiang-Fu Yu, Yiming Yang, I. Dhillon•Mon May 06 2019

X-Transformer is proposed, the first scalable approach to fine-tuning deep transformer models for the XMC problem and achieves new state-of-the-art results on four XMC benchmark datasets.

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Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization

Venkatesh Umaashankar, Girish Shanmugam, Aditi Prakash•Sun Aug 11 2019

This paper introduces a high-quality product taxonomy dataset focusing on clothing products which contain 186,150 images under clothing category with 3 levels and 52 leaf nodes in the taxonomy and establishes the benchmark by comparing image classification and Attention based Sequence models for predicting the category path.

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Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

Song Wang, Zhen Tan, Jundong Li, Kaize Ding, Huan Liu•Sat Dec 10 2022

Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol.

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SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation

Abhishek Divekar, Greg Durrett•Wed May 15 2024

This work proposes Synthesize by Retrieval and Refinement (SynthesizRR), which uses retrieval augmentation to introduce variety into the dataset synthesis process: as retrieved passages vary, the LLM is seeded with different content to generate its examples.

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