Unraveling Key Factors of Knowledge Distillation

• URL: http://arxiv.org/abs/2312.08585v2
• Date: Sun, 24 Dec 2023 03:34:31 GMT
• Title: Unraveling Key Factors of Knowledge Distillation
• Authors: Jingxuan Wei, Linzhuang Sun, Xu Tan, Bihui Yu, Ruifeng Guo
• Abstract summary: This study investigates how student model capacity, data complexity, and decoding strategies collectively influence distillation effectiveness. We empirically validate hypotheses related to the impact of these factors on knowledge distillation. Our research not only elucidates the significant influence of model capacity, data complexity, and decoding strategies on distillation effectiveness but also introduces a novel, optimized distillation approach.
• Score: 19.29311840930773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT). However, existing research primarily focuses on empirical applications, and there is a lack of comprehensive understanding of how student model capacity, data complexity, and decoding strategies collectively influence distillation effectiveness. Addressing this gap, our study conducts an in-depth investigation into these factors, particularly focusing on their interplay in word-level and sequence-level distillation within NMT. Through extensive experimentation across datasets like IWSLT13 En$\rightarrow$Fr, IWSLT14 En$\rightarrow$De, and others, we empirically validate hypotheses related to the impact of these factors on knowledge distillation. Our research not only elucidates the significant influence of model capacity, data complexity, and decoding strategies on distillation effectiveness but also introduces a novel, optimized distillation approach. This approach, when applied to the IWSLT14 de$\rightarrow$en translation task, achieves state-of-the-art performance, demonstrating its practical efficacy in advancing the field of NMT.

Related papers

• The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model [2.355460994057843]
  Self-distillation (SD) is a technique where a model refines itself from its own predictions. Despite its widespread use, the mechanisms underlying its effectiveness remain unclear.
  arXiv  Detail & Related papers  (2025-01-27T17:20:48Z)
• Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration [74.09687562334682]
  We introduce a novel training data attribution method called Debias and Denoise Attribution (DDA) Our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral.
  arXiv  Detail & Related papers  (2024-10-02T07:14:26Z)
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy [0.8070353314073375]
  The current focus in Autonomous Experimentation (AE) is on developing robust to conduct the AE effectively. This paper presents an analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Learning (DKL) We illustrate the impact of the'seed effect' and in-loop seed interventions on the effectiveness of DKL in predicting material properties.
  arXiv  Detail & Related papers  (2024-01-30T20:08:15Z)
• Knowledge Distillation via Token-level Relationship Graph [12.356770685214498]
  We propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-20T08:16:37Z)
• Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples [0.0]
  This research paper explores the impact of various input parameters, including Infill percentage, Layer Height, Extrusion Temperature, and Print Speed, on the resulting Tensile Strength in objects produced through additive manufacturing. We introduce the utilization of Explainable Artificial Intelligence (XAI) techniques for the first time, which allowed us to analyze the data and gain valuable insights into the system's behavior. Our findings reveal that the Infill percentage and Extrusion Temperature have the most significant influence on Tensile Strength, while the impact of Layer Height and Print Speed is relatively minor.
  arXiv  Detail & Related papers  (2023-05-28T21:44:25Z)
• Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation [96.92250565207017]
  We study the data efficiency and selection for the dataset distillation task. By re-formulating the dynamics of distillation, we provide insight into the inherent redundancy in the real dataset. We find the most contributing samples based on their causal effects on the distillation.
  arXiv  Detail & Related papers  (2023-05-28T06:53:41Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)
• A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness [8.432686179800543]
  We conduct extensive experiments to evaluate current state-of-the-art dataset distillation methods. We successfully use membership inference attacks to show that privacy risks still remain. This work offers a large-scale benchmarking framework for dataset distillation evaluation.
  arXiv  Detail & Related papers  (2023-05-05T08:19:27Z)
• Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset [63.20765930558542]
  Active Learning has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. We evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection. We also study the effect of application of data augmentation within Bayesian AL based dataset distillation.
  arXiv  Detail & Related papers  (2023-02-21T13:56:47Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Residual Knowledge Distillation [96.18815134719975]
  This work proposes Residual Knowledge Distillation (RKD), which further distills the knowledge by introducing an assistant (A) In this way, S is trained to mimic the feature maps of T, and A aids this process by learning the residual error between them. Experiments show that our approach achieves appealing results on popular classification datasets, CIFAR-100 and ImageNet.
  arXiv  Detail & Related papers  (2020-02-21T07:49:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.