Relational Representation Distillation

• URL: http://arxiv.org/abs/2407.12073v3
• Date: Mon, 9 Sep 2024 06:13:48 GMT
• Title: Relational Representation Distillation
• Authors: Nikolaos Giakoumoglou, Tania Stathaki,
• Abstract summary: We introduce Representation Distillation (RRD) to explore and reinforce relationships between teacher and student models. Inspired by self-supervised learning principles, it uses a relaxed contrastive loss that focuses on similarity than exact replication. Our approach demonstrates superior performance on CIFAR-100 and ImageNet ILSVRC-2012 and sometimes even outperforms the teacher network when combined with KD.
• Score: 6.24302896438145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge distillation (KD) is an effective method for transferring knowledge from a large, well-trained teacher model to a smaller, more efficient student model. Despite its success, one of the main challenges in KD is ensuring the efficient transfer of complex knowledge while maintaining the student's computational efficiency. Unlike previous works that applied contrastive objectives promoting explicit negative instances with little attention to the relationships between them, we introduce Relational Representation Distillation (RRD). Our approach leverages pairwise similarities to explore and reinforce the relationships between the teacher and student models. Inspired by self-supervised learning principles, it uses a relaxed contrastive loss that focuses on similarity rather than exact replication. This method aligns the output distributions of teacher samples in a large memory buffer, improving the robustness and performance of the student model without the need for strict negative instance differentiation. Our approach demonstrates superior performance on CIFAR-100 and ImageNet ILSVRC-2012, outperforming traditional KD and sometimes even outperforms the teacher network when combined with KD. It also transfers successfully to other datasets like Tiny ImageNet and STL-10. Code is available at https://github.com/giakoumoglou/distillers.

Related papers

• Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling [81.00825302340984]
  We introduce Speculative Knowledge Distillation (SKD) to generate high-quality training data on-the-fly. In SKD, the student proposes tokens, and the teacher replaces poorly ranked ones based on its own distribution. We evaluate SKD on various text generation tasks, including translation, summarization, math, and instruction following.
  arXiv  Detail & Related papers  (2024-10-15T06:51:25Z)
• Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models [62.5501109475725]
  Knowledge distillation (KD) is a technique that compresses large teacher models by training smaller student models to mimic them. This paper introduces Online Knowledge Distillation (OKD), where the teacher network integrates small online modules to concurrently train with the student model. OKD achieves or exceeds the performance of leading methods in various model architectures and sizes, reducing training time by up to fourfold.
  arXiv  Detail & Related papers  (2024-09-19T07:05:26Z)
• Invariant Causal Knowledge Distillation in Neural Networks [6.24302896438145]
  In this paper, we introduce Invariant Consistency Distillation (ICD), a novel methodology designed to enhance knowledge distillation. ICD ensures that the student model's representations are both discriminative and invariant with respect to the teacher's outputs. Our results on CIFAR-100 and ImageNet ILSVRC-2012 show that ICD outperforms traditional KD techniques and surpasses state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-16T14:53:35Z)
• Relative Difficulty Distillation for Semantic Segmentation [54.76143187709987]
  We propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD) RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals. Our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound.
  arXiv  Detail & Related papers  (2024-07-04T08:08:25Z)
• Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning [3.1423836318272773]
  Knowledge distillation (KD) improves the performance of efficient and lightweight models. Most existing KD techniques rely on Kullback-Leibler (KL) divergence. We propose a Robustness-Reinforced Knowledge Distillation (R2KD) that leverages correlation distance and network pruning.
  arXiv  Detail & Related papers  (2023-11-23T11:34:48Z)
• Comparative Knowledge Distillation [102.35425896967791]
  Traditional Knowledge Distillation (KD) assumes readily available access to teacher models for frequent inference. We propose Comparative Knowledge Distillation (CKD), which encourages student models to understand the nuanced differences in a teacher model's interpretations of samples. CKD consistently outperforms state of the art data augmentation and KD techniques.
  arXiv  Detail & Related papers  (2023-11-03T21:55:33Z)
• Improved knowledge distillation by utilizing backward pass knowledge in neural networks [17.437510399431606]
  Knowledge distillation (KD) is one of the prominent techniques for model compression. In this work, we generate new auxiliary training samples based on extracting knowledge from the backward pass of the teacher. We show how this technique can be used successfully in applications of natural language processing (NLP) and language understanding.
  arXiv  Detail & Related papers  (2023-01-27T22:07:38Z)
• DisCo: Effective Knowledge Distillation For Contrastive Learning of Sentence Embeddings [36.37939188680754]
  We propose an enhanced knowledge distillation framework termed Distill-Contrast (DisCo) DisCo transfers the capability of a large sentence embedding model to a small student model on large unlabelled data. We also propose Contrastive Knowledge Distillation (CKD) to enhance the consistencies among teacher model training, KD, and student model finetuning.
  arXiv  Detail & Related papers  (2021-12-10T16:11:23Z)
• How and When Adversarial Robustness Transfers in Knowledge Distillation? [137.11016173468457]
  This paper studies how and when the adversarial robustness can be transferred from a teacher model to a student model in Knowledge distillation (KD) We show that standard KD training fails to preserve adversarial robustness, and we propose KD with input gradient alignment (KDIGA) for remedy. Under certain assumptions, we prove that the student model using our proposed KDIGA can achieve at least the same certified robustness as the teacher model.
  arXiv  Detail & Related papers  (2021-10-22T21:30:53Z)
• MixKD: Towards Efficient Distillation of Large-scale Language Models [129.73786264834894]
  We propose MixKD, a data-agnostic distillation framework, to endow the resulting model with stronger generalization ability. We prove from a theoretical perspective that under reasonable conditions MixKD gives rise to a smaller gap between the error and the empirical error. Experiments under a limited-data setting and ablation studies further demonstrate the advantages of the proposed approach.
  arXiv  Detail & Related papers  (2020-11-01T18:47:51Z)
• Learning from a Lightweight Teacher for Efficient Knowledge Distillation [14.865673786025525]
  This paper proposes LW-KD, short for lightweight knowledge distillation. It firstly trains a lightweight teacher network on a synthesized simple dataset, with an adjustable class number equal to that of a target dataset. The teacher then generates soft target whereby an enhanced KD loss could guide student learning, which is a combination of KD loss and adversarial loss for making student output indistinguishable from the output of the teacher.
  arXiv  Detail & Related papers  (2020-05-19T01:54:15Z)

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.