Papers by Divya Jyoti Bajpai
CapEEN: Image Captioning with Early Exits and Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Early Exit (EE) strategies can be used to enhance their efficiency, but their adaptation presents challenges in image captioning as it requires varying levels of semantic information for accurate predictions. |
| Approach: | They propose a framework to improve the performance of EE strategies by knowledge distillation . they use a variant A-CapEEN to adapt thresholds on the fly to account for drifts . |
| Outcome: | The proposed framework gains speedup of 1.77 while maintaining competitive performance compared to the final layer. |
CeeBERT: Cross-Domain Inference in Early Exit BERT (2024.findings-acl)
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| Challenge: | Pre-trained Language Models suffer in inference latency due to their large size. |
| Approach: | They propose an online learning algorithm that dynamically determines early exits of samples based on the level of confidence observed at intermediate layers. |
| Outcome: | The proposed algorithm can speed up the BERT/ALBERT models by 2 - 3.1 with minimal drop in accuracy. |
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs (2024.findings-emnlp)
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| Challenge: | Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability but their large size results in high inference latency. |
| Approach: | They propose an unsupervised domain adaptation framework that employs knowledge distillation to achieve domain-invariant representations at each layer. |
| Outcome: | The proposed framework outperforms early exit methods and domain adaptation methods under domain shift scenarios. |
FREE: Fast and Robust Vision Language Models with Early Exits (2025.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-language tasks, but their large size poses challenges for real-world applications. |
| Approach: | They propose an adversarial approach to train exit classifiers in Vision-Language Models that uses a transformer layer and a classifier to perform input-adaptive inference. |
| Outcome: | The proposed approach speeds up inference speed with minimal drop in performance by 1.51 while retaining comparable performance. |
FAIR: Filtering of Automatically Induced Rules (2024.eacl-long)
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| Challenge: | Existing methods to reduce the human annotation efforts require a diverse set of rules to assign labels to unlabeled data. |
| Approach: | They propose an automatic rule-filtering algorithm to filter out a large set of automatically created rules from a small set of labeled features. |
| Outcome: | The proposed approach achieves statistically significant results over existing methods. |