KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910705218748416 |
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| author | Bora, Maheswar Atreya, Saurabh Mukherjee, Aritra Das, Abhijit |
| author_facet | Bora, Maheswar Atreya, Saurabh Mukherjee, Aritra Das, Abhijit |
| contents | In this work, we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning, self-distillation (knowledge distillation) and masked data modelling, the three major SSL frameworks, to learn a joint and coordinated representation. The proposed technique of SSL learns by the collaborative power of different learning objectives of SSL. Hence to jointly learn the different SSL objectives we proposed a new SSL architecture KDC-MAE, a complementary masking strategy to learn the modular correspondence, and a weighted way to combine them coordinately. Experimental results conclude that the contrastive masking correspondence along with the KD learning objective has lent a hand to performing better learning for multiple modalities over multiple tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_12270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder Bora, Maheswar Atreya, Saurabh Mukherjee, Aritra Das, Abhijit Computer Vision and Pattern Recognition In this work, we attempted to extend the thought and showcase a way forward for the Self-supervised Learning (SSL) learning paradigm by combining contrastive learning, self-distillation (knowledge distillation) and masked data modelling, the three major SSL frameworks, to learn a joint and coordinated representation. The proposed technique of SSL learns by the collaborative power of different learning objectives of SSL. Hence to jointly learn the different SSL objectives we proposed a new SSL architecture KDC-MAE, a complementary masking strategy to learn the modular correspondence, and a weighted way to combine them coordinately. Experimental results conclude that the contrastive masking correspondence along with the KD learning objective has lent a hand to performing better learning for multiple modalities over multiple tasks. |
| title | KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.12270 |