Fashion
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Classification | Fashion (test) | Accuracy99.35 | 51 | |
| Next-Item Recommendation | Fashion Amazon (test) | HR@100.661 | 29 | |
| Density Estimation | Fashion (test) | NLL (bits/dim)2.803 | 27 | |
| Classification | fashion | F1 Score90.04 | 26 | |
| Multi-turn information acquisition | Fashion | Success Rate (SR)83.77 | 25 | |
| Multi-view Clustering | Fashion | ACC99.2 | 25 | |
| Clustering | Fashion (full) | ACC67.2 | 24 | |
| Text-to-Image Retrieval | Fashion200K | Recall@1031.39 | 18 | |
| Clustering | Fashion | Accuracy75.42 | 17 | |
| Image Generation | Fashion (test) | FID10.3 | 16 | |
| Sequential Recommendation | Fashion | Recall17.67 | 14 | |
| Binary Classification | Fashion (test) | AUC98.2 | 13 | |
| Clustering | FASHION | Accuracy72.1 | 12 | |
| Classification | fashion | F1 Macro89.91 | 12 | |
| Classification | fashion | Accuracy91.35 | 12 | |
| Multi-view Clustering | Fashion V=3 N=10000 | ACC99.74 | 11 | |
| Clustering | Fashion | Running Time0.98 | 10 | |
| kNN | Fashion | Throughput (QPS)40,982 | 10 | |
| Predicting Generalization | Fashion PGDL (train test) | CMI2.35 | 10 | |
| Pose-guided Video Generation | Fashion (test) | PSNR23.9 | 9 | |
| Unsupervised Online Label Shift | Fashion Square shift | Classification Error4.1 | 9 | |
| Unsupervised Online Label Shift | Fashion Monotone shift | Classification Error5.1 | 9 | |
| Unsupervised Online Label Shift | Fashion (holdout) | Classification Error (Ber)3.5 | 9 | |
| Unsupervised Online Label Shift | Fashion | Classification Error (Ber)3.7 | 9 | |
| Recommendation | Fashion | Hit Rate@206.07 | 8 |
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