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--- |
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license: mit |
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datasets: |
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- imagenet-1k |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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tags: |
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- code |
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--- |
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# Matryoshka Representation Learning🪆 |
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_Aditya Kusupati*, Gantavya Bhatt*, Aniket Rege*, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi_ |
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GitHub: https://github.com/RAIVNLab/MRL |
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Arxiv: https://arxiv.org/abs/2205.13147 |
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We provide pretrained models trained with [FFCV](https://github.com/libffcv/ffcv) on ImageNet-1K: |
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1. `mrl` : ResNet50 __mrl__ models trained with Matryoshka loss (vanilla and efficient) with nesting starting from _d=8_ (default) and _d=16_ |
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2. `fixed-feature` : independently trained ResNet50 baselines at _log(d)_ granularities |
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3. `resnet-family` : __mrl__ and __ff__ models trained on ResNet18/34/101 |
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## Citation |
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If you find this project useful in your research, please consider citing: |
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``` |
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@inproceedings{kusupati2022matryoshka, |
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title = {Matryoshka Representation Learning}, |
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author = {Kusupati, Aditya and Bhatt, Gantavya and Rege, Aniket and Wallingford, Matthew and Sinha, Aditya and Ramanujan, Vivek and Howard-Snyder, William and Chen, Kaifeng and Kakade, Sham and Jain, Prateek and others}, |
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title = {Matryoshka Representation Learning.}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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month = {December}, |
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year = {2022}, |
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} |
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``` |
