Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers

• URL: http://arxiv.org/abs/2502.07436v1
• Date: Tue, 11 Feb 2025 10:24:57 GMT
• Title: Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers
• Authors: Zhaodong Bing, Linze Li, Jiajun Liang,
• Abstract summary: Existing methods either require identical head counts or introduce projectors to bridge dimensional gaps. We propose Squeezing-Heads Distillation (SHD), a novel approach that enables seamless knowledge transfer between models with varying head counts.
• Score: 7.386296525051779
• License:
• Abstract: Knowledge distillation (KD) in transformers often faces challenges due to misalignment in the number of attention heads between teacher and student models. Existing methods either require identical head counts or introduce projectors to bridge dimensional gaps, limiting flexibility and efficiency. We propose Squeezing-Heads Distillation (SHD), a novel approach that enables seamless knowledge transfer between models with varying head counts by compressing multi-head attention maps via efficient linear approximation. Unlike prior work, SHD eliminates alignment barriers without additional parameters or architectural modifications. Our method dynamically approximates the combined effect of multiple teacher heads into fewer student heads, preserving fine-grained attention patterns while reducing redundancy. Experiments across language (LLaMA, GPT) and vision (DiT, MDT) generative and vision (DeiT) discriminative tasks demonstrate SHD's effectiveness: it outperforms logit-based and feature-alignment KD baselines, achieving state-of-the-art results in image classification, image generation language fine-tuning, and language pre-training. The key innovations of flexible head compression, projector-free design, and linear-time complexity make SHD a versatile and scalable solution for distilling modern transformers. This work bridges a critical gap in KD, enabling efficient deployment of compact models without compromising performance.

Related papers

• Active Data Curation Effectively Distills Large-Scale Multimodal Models [66.23057263509027]
  Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. In this work we explore an alternative, yet simple approach -- active data curation as effective distillation for contrastive multimodal pretraining. Our simple online batch selection method, ACID, outperforms strong KD baselines across various model-, data- and compute-configurations.
  arXiv  Detail & Related papers  (2024-11-27T18:50:15Z)
• Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation [10.48108719012248]
  We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model. In contrast to much of the previous work, we scale up the parameters of the student model during training.
  arXiv  Detail & Related papers  (2024-11-10T12:40:59Z)
• TAS: Distilling Arbitrary Teacher and Student via a Hybrid Assistant [52.0297393822012]
  We introduce an assistant model as a bridge to facilitate smooth feature knowledge transfer between heterogeneous teachers and students. Within our proposed design principle, the assistant model combines the advantages of cross-architecture inductive biases and module functions. Our proposed method is evaluated across some homogeneous model pairs and arbitrary heterogeneous combinations of CNNs, ViTs, spatial KDs.
  arXiv  Detail & Related papers  (2024-10-16T08:02:49Z)
• Visual Prompt Tuning in Null Space for Continual Learning [51.96411454304625]
  Existing prompt-tuning methods have demonstrated impressive performances in continual learning (CL) This paper aims to learn each task by tuning the prompts in the direction orthogonal to the subspace spanned by previous tasks' features. In practice, an effective null-space-based approximation solution has been proposed to implement the prompt gradient projection.
  arXiv  Detail & Related papers  (2024-06-09T05:57:40Z)
• Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation [4.242540533823568]
  Transformer models are usually computationally-expensive, and their effectiveness in light-weight models are limited compared to convolutions. We propose a cross-architecture knowledge distillation method for MDE, dubbed DisDepth, to enhance efficient CNN models with the supervision of state-of-the-art transformer models. Our method achieves significant improvements on various efficient backbones, showcasing its potential for efficient monocular depth estimation.
  arXiv  Detail & Related papers  (2024-04-25T07:55:47Z)
• SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer [102.39050180060913]
  Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. Recent breakthroughs have been driven by mask strategy that significantly improves the training efficiency of DiT with additional intra-image contextual learning. In this work, we address these limitations by novelly unleashing the self-supervised discrimination knowledge to boost DiT training.
  arXiv  Detail & Related papers  (2024-03-25T17:59:35Z)
• Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation [3.759878064139572]
  We introduce the 'Align-to-Distill' (A2D) strategy to address the feature mapping problem. Our experiments show the efficacy of A2D, demonstrating gains of up to +3.61 and +0.63 BLEU points for WMT-2022->Dsb and WMT-2014 En->De.
  arXiv  Detail & Related papers  (2024-03-03T11:13:44Z)
• Solving Continual Offline Reinforcement Learning with Decision Transformer [78.59473797783673]
  Continuous offline reinforcement learning (CORL) combines continuous and offline reinforcement learning. Existing methods, employing Actor-Critic structures and experience replay (ER), suffer from distribution shifts, low efficiency, and weak knowledge-sharing. We introduce multi-head DT (MH-DT) and low-rank adaptation DT (LoRA-DT) to mitigate DT's forgetting problem.
  arXiv  Detail & Related papers  (2024-01-16T16:28:32Z)
• Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression [6.508088032296086]
  Vision Transformers (ViTs) offer the tantalizing prospect of unified information processing across visual and textual domains. We introduce an innovative ensemble-based distillation approach distilling inductive bias from complementary lightweight teacher models. Our proposed framework also involves precomputing and storing logits in advance, essentially the unnormalized predictions of the model.
  arXiv  Detail & Related papers  (2023-09-30T13:21:29Z)
• 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)

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.