Quantifying Knowledge Distillation Using Partial Information Decomposition

• URL: http://arxiv.org/abs/2411.07483v1
• Date: Tue, 12 Nov 2024 02:12:41 GMT
• Title: Quantifying Knowledge Distillation Using Partial Information Decomposition
• Authors: Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta,
• Abstract summary: Knowledge distillation provides an effective method for deploying complex machine learning models in resource-constrained environments. We quantify the distillable and distilled knowledge of a teacher's representation corresponding to a given student and a downstream task. We demonstrate that this metric can be practically used in distillation to address challenges caused by the complexity gap between the teacher and the student representations.
• Score: 14.82261635235695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge distillation provides an effective method for deploying complex machine learning models in resource-constrained environments. It typically involves training a smaller student model to emulate either the probabilistic outputs or the internal feature representations of a larger teacher model. By doing so, the student model often achieves substantially better performance on a downstream task compared to when it is trained independently. Nevertheless, the teacher's internal representations can also encode noise or additional information that may not be relevant to the downstream task. This observation motivates our primary question: What are the information-theoretic limits of knowledge transfer? To this end, we leverage a body of work in information theory called Partial Information Decomposition (PID) to quantify the distillable and distilled knowledge of a teacher's representation corresponding to a given student and a downstream task. Moreover, we demonstrate that this metric can be practically used in distillation to address challenges caused by the complexity gap between the teacher and the student representations.

Related papers

• Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation [64.15918654558816]
  Self-distillation (SSD) training strategy is introduced for filtering and weighting teacher representation to distill from task-relevant representations only. Experimental results on real-world affective computing, wearable/biosignal datasets from the UCR Archive, the HAR dataset, and image classification datasets show that the proposed SSD method can outperform state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-19T14:08:56Z)
• Student-Oriented Teacher Knowledge Refinement for Knowledge Distillation [11.754014876977422]
  This paper introduces a novel perspective emphasizing student-oriented and refining the teacher's knowledge to better align with the student's needs. We present the Student-Oriented Knowledge Distillation (SoKD), which incorporates a learnable feature augmentation strategy during training. We also deploy the Distinctive Area Detection Module (DAM) to identify areas of mutual interest between the teacher and student.
  arXiv  Detail & Related papers  (2024-09-27T14:34:08Z)
• Leveraging Superfluous Information in Contrastive Representation Learning [0.0]
  We show that superfluous information does exist during the conventional contrastive learning framework. We design a new objective, namely SuperInfo, to learn robust representations by a linear combination of both predictive and superfluous information. We demonstrate that learning with our loss can often outperform the traditional contrastive learning approaches on image classification, object detection and instance segmentation tasks.
  arXiv  Detail & Related papers  (2024-08-19T16:21:08Z)
• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image Segmentation [0.0]
  This thesis aims to investigate the feasibility of knowledge transfer between neural networks for medical image segmentation tasks. In the context of medical imaging, where the data volumes are often limited, leveraging knowledge from a larger pre-trained network could be useful.
  arXiv  Detail & Related papers  (2024-06-05T12:06:04Z)
• Exploring Graph-based Knowledge: Multi-Level Feature Distillation via Channels Relational Graph [8.646512035461994]
  In visual tasks, large teacher models capture essential features and deep information, enhancing performance. We propose a distillation framework based on graph knowledge, including a multi-level feature alignment strategy. We emphasize spectral embedding (SE) as a key technique in our distillation process, which merges the student's feature space with the relational knowledge and structural complexities similar to the teacher network.
  arXiv  Detail & Related papers  (2024-05-14T12:37:05Z)
• Can a student Large Language Model perform as well as it's teacher? [0.0]
  Knowledge distillation aims to transfer knowledge from a high-capacity "teacher" model to a streamlined "student" model. This paper provides a comprehensive overview of the knowledge distillation paradigm.
  arXiv  Detail & Related papers  (2023-10-03T20:34:59Z)
• 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)
• Knowledge Diffusion for Distillation [53.908314960324915]
  The representation gap between teacher and student is an emerging topic in knowledge distillation (KD) We state that the essence of these methods is to discard the noisy information and distill the valuable information in the feature. We propose a novel KD method dubbed DiffKD, to explicitly denoise and match features using diffusion models.
  arXiv  Detail & Related papers  (2023-05-25T04:49:34Z)
• Distillation from Heterogeneous Models for Top-K Recommendation [43.83625440616829]
  HetComp is a framework that guides the student model by transferring sequences of knowledge from teachers' trajectories. HetComp significantly improves the distillation quality and the generalization of the student model.
  arXiv  Detail & Related papers  (2023-03-02T10:23:50Z)
• Prototype-guided Cross-task Knowledge Distillation for Large-scale Models [103.04711721343278]
  Cross-task knowledge distillation helps to train a small student model to obtain a competitive performance. We propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios.
  arXiv  Detail & Related papers  (2022-12-26T15:00:42Z)
• Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution [82.89021683451432]
  We propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods.
  arXiv  Detail & Related papers  (2022-07-18T02:41:04Z)
• Knowledge Distillation Meets Open-Set Semi-Supervised Learning [69.21139647218456]
  We propose a novel em modelname (bfem shortname) method dedicated for distilling representational knowledge semantically from a pretrained teacher to a target student. At the problem level, this establishes an interesting connection between knowledge distillation with open-set semi-supervised learning (SSL) Our shortname outperforms significantly previous state-of-the-art knowledge distillation methods on both coarse object classification and fine face recognition tasks.
  arXiv  Detail & Related papers  (2022-05-13T15:15:27Z)
• On the benefits of knowledge distillation for adversarial robustness [53.41196727255314]
  We show that knowledge distillation can be used directly to boost the performance of state-of-the-art models in adversarial robustness. We present Adversarial Knowledge Distillation (AKD), a new framework to improve a model's robust performance.
  arXiv  Detail & Related papers  (2022-03-14T15:02:13Z)
• Dynamic Rectification Knowledge Distillation [0.0]
  Dynamic Rectification Knowledge Distillation (DR-KD) is a knowledge distillation framework. DR-KD transforms the student into its own teacher, and if the self-teacher makes wrong predictions while distilling information, the error is rectified prior to the knowledge being distilled. Our proposed DR-KD performs remarkably well in the absence of a sophisticated cumbersome teacher model.
  arXiv  Detail & Related papers  (2022-01-27T04:38:01Z)
• Wasserstein Contrastive Representation Distillation [114.24609306495456]
  We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for knowledge distillation. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. Experiments demonstrate that the proposed WCoRD method outperforms state-of-the-art approaches on privileged information distillation, model compression and cross-modal transfer.
  arXiv  Detail & Related papers  (2020-12-15T23:43:28Z)
• Knowledge Distillation Meets Self-Supervision [109.6400639148393]
  Knowledge distillation involves extracting "dark knowledge" from a teacher network to guide the learning of a student network. We show that the seemingly different self-supervision task can serve as a simple yet powerful solution. By exploiting the similarity between those self-supervision signals as an auxiliary task, one can effectively transfer the hidden information from the teacher to the student.
  arXiv  Detail & Related papers  (2020-06-12T12:18:52Z)

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.