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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Going deeper with Image Transformers |
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| Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles |
| 2021 |
| arxiv 2103.17239 |
| Scalable domain adaptation of convolutional neural networks | Adrian Popescu, Etienne Gadeski, Hervé Le Borgne | 2015 | arxiv 1512.02013 |
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| Exploring the Limits of Weakly Supervised Pretraining | Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan | 2018 | arxiv 1805.00932 |
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| ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification | Miko\laj Janusz, Adam Wróbel, Bartosz Zieliński | 2026 | arxiv 2602.06592 |
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| Effective and Efficient Masked Image Generation Models | Zebin You, Jingyang Ou, Xiaolu Zhang | 2025 | arxiv 2503.07197 |
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| NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization | Hao Lu, Yongxin Guo, Onur Koyun | 2026 | arxiv 2606.11363 |
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| Deep Nearest Neighbor Anomaly Detection | Liron Bergman, Niv Cohen, Yedid Hoshen | 2020 | arxiv 2002.10445 |
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| Temporal-adaptive Weight Quantization for Spiking Neural Networks | Han Zhang, Qingyan Meng, Jiaqi Wang | 2025 | arxiv 2511.17567 |
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| RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training | Jean Lahoud, Bernard Ghanem | 2020 | arxiv 2002.02200 |
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| SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization | Bowen Yuan, Yuxia Fu, Zijian Wang | 2025 | arxiv 2503.13935 |
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