Boosting Light-Weight Depth Estimation Via Knowledge Distillation

• URL: http://arxiv.org/abs/2105.06143v3
• Date: Sun, 16 Apr 2023 06:41:31 GMT
• Title: Boosting Light-Weight Depth Estimation Via Knowledge Distillation
• Authors: Junjie Hu, Chenyou Fan, Hualie Jiang, Xiyue Guo, Yuan Gao, Xiangyong Lu, and Tin Lun Lam
• Abstract summary: We propose a lightweight network that can accurately estimate depth maps using minimal computing resources. We achieve this by designing a compact model architecture that maximally reduces model complexity. Our method achieves comparable performance to state-of-the-art methods while using only 1% of their parameters.
• Score: 21.93879961636064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular depth estimation (MDE) methods are often either too computationally expensive or not accurate enough due to the trade-off between model complexity and inference performance. In this paper, we propose a lightweight network that can accurately estimate depth maps using minimal computing resources. We achieve this by designing a compact model architecture that maximally reduces model complexity. To improve the performance of our lightweight network, we adopt knowledge distillation (KD) techniques. We consider a large network as an expert teacher that accurately estimates depth maps on the target domain. The student, which is the lightweight network, is then trained to mimic the teacher's predictions. However, this KD process can be challenging and insufficient due to the large model capacity gap between the teacher and the student. To address this, we propose to use auxiliary unlabeled data to guide KD, enabling the student to better learn from the teacher's predictions. This approach helps fill the gap between the teacher and the student, resulting in improved data-driven learning. Our extensive experiments show that our method achieves comparable performance to state-of-the-art methods while using only 1% of their parameters. Furthermore, our method outperforms previous lightweight methods regarding inference accuracy, computational efficiency, and generalizability.

Related papers

• 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)
• 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)
• EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval [83.79667141681418]
  Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR) We propose a novel distillation approach that leverages the relative geometry among queries and documents learned by the large teacher model. We show that our approach successfully distills from both dual-encoder (DE) and cross-encoder (CE) teacher models to 1/10th size asymmetric students that can retain 95-97% of the teacher performance.
  arXiv  Detail & Related papers  (2023-01-27T22:04:37Z)
• CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation [4.182345120164705]
  This paper proposes a new technique called Curriculum Expert Selection for Knowledge Distillation (CES-KD) CES-KD is built upon the hypothesis that a student network should be guided gradually using stratified teaching curriculum. Specifically, our method is a gradual TA-based KD technique that selects a single teacher per input image based on a curriculum driven by the difficulty in classifying the image.
  arXiv  Detail & Related papers  (2022-09-15T21:02:57Z)
• Knowledge Distillation with Representative Teacher Keys Based on Attention Mechanism for Image Classification Model Compression [1.503974529275767]
  knowledge distillation (KD) has been recognized as one of the effective method of model compression to decrease the model parameters. Inspired by attention mechanism, we propose a novel KD method called representative teacher key (RTK) Our proposed RTK can effectively improve the classification accuracy of the state-of-the-art attention-based KD method.
  arXiv  Detail & Related papers  (2022-06-26T05:08:50Z)
• Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation [70.92135839545314]
  We propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation. Our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
  arXiv  Detail & Related papers  (2022-06-13T11:52:13Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• Modality-specific Distillation [30.190082262375395]
  We propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets. Our idea aims at mimicking a teacher's modality-specific predictions by introducing an auxiliary loss term for each modality. Because each modality has different importance for predictions, we also propose weighting approaches for the auxiliary losses.
  arXiv  Detail & Related papers  (2021-01-06T05:45:07Z)
• Mixed-Privacy Forgetting in Deep Networks [114.3840147070712]
  We show that the influence of a subset of the training samples can be removed from the weights of a network trained on large-scale image classification tasks. Inspired by real-world applications of forgetting techniques, we introduce a novel notion of forgetting in mixed-privacy setting. We show that our method allows forgetting without having to trade off the model accuracy.
  arXiv  Detail & Related papers  (2020-12-24T19:34:56Z)
• 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)
• Heterogeneous Knowledge Distillation using Information Flow Modeling [82.83891707250926]
  We propose a novel KD method that works by modeling the information flow through the various layers of the teacher model. The proposed method is capable of overcoming the aforementioned limitations by using an appropriate supervision scheme during the different phases of the training process.
  arXiv  Detail & Related papers  (2020-05-02T06:56:56Z)

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.