Equally Critical: Samples, Targets, and Their Mappings in Datasets

• URL: http://arxiv.org/abs/2506.01987v1
• Date: Sat, 17 May 2025 08:27:19 GMT
• Title: Equally Critical: Samples, Targets, and Their Mappings in Datasets
• Authors: Runkang Yang, Peng Sun, Xinyi Shang, Yi Tang, Tao Lin,
• Abstract summary: This paper investigates how sample and target collectively influence training dynamic.<n>We first establish a taxonomy of existing paradigms through the lens of sample-target interactions.<n>We then propose a novel unified loss framework to assess their impact on training efficiency.
• Score: 6.859656302020063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data inherently possesses dual attributes: samples and targets. For targets, knowledge distillation has been widely employed to accelerate model convergence, primarily relying on teacher-generated soft target supervision. Conversely, recent advancements in data-efficient learning have emphasized sample optimization techniques, such as dataset distillation, while neglected the critical role of target. This dichotomy motivates our investigation into understanding how both sample and target collectively influence training dynamic. To address this gap, we first establish a taxonomy of existing paradigms through the lens of sample-target interactions, categorizing them into distinct sample-to-target mapping strategies. Building upon this foundation, we then propose a novel unified loss framework to assess their impact on training efficiency. Through extensive empirical studies on our proposed strategies, we comprehensively analyze how variations in target and sample types, quantities, and qualities influence model training, providing six key insights to enhance training efficacy.

Related papers

• How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations [69.72654127617058]
  Post-hoc importance attribution methods are a popular tool for "explaining" Deep Neural Networks (DNNs)<n>In this work we bring forward empirical evidence that challenges this very notion.<n>We discover a strong dependency on and demonstrate that the training details of a pre-trained model's classification layer play a crucial role.
  arXiv  Detail & Related papers  (2025-03-01T22:25:11Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance.<n>We introduce novel algorithms for dynamic, instance-level data reweighting.<n>Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation [20.172605920901777]
  This paper focuses on designing a framework to use different strategies for comprehensively mining different target samples. We propose a novel source-free framework (SOUF) to achieve semi-supervised fine-tuning of the source pre-trained model on the target domain.
  arXiv  Detail & Related papers  (2024-11-11T02:09:32Z)
• Most Influential Subset Selection: Challenges, Promises, and Beyond [9.479235005673683]
  We study the Most Influential Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence.<n>We conduct a comprehensive analysis of the prevailing approaches in MISS, elucidating their strengths and weaknesses.<n>We demonstrate that an adaptive version of theses which applies them iteratively, can effectively capture the interactions among samples.
  arXiv  Detail & Related papers  (2024-09-25T20:00:23Z)
• An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning [36.619804184427245]
  Class-Incremental Learning (CIL) aims to build classification models from data streams. Due to catastrophic forgetting, CIL is particularly challenging when examples from past classes cannot be stored. Use of models pre-trained in a self-supervised way on large amounts of data has recently gained momentum.
  arXiv  Detail & Related papers  (2023-08-22T14:06:40Z)
• Ensemble Modeling for Multimodal Visual Action Recognition [50.38638300332429]
  We propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored to handle the long-tailed distribution of the MECCANO [21] dataset.
  arXiv  Detail & Related papers  (2023-08-10T08:43:20Z)
• Universal Semi-supervised Model Adaptation via Collaborative Consistency Training [92.52892510093037]
  We introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA) We propose a collaborative consistency training framework that regularizes the prediction consistency between two models. Experimental results demonstrate the effectiveness of our method on several benchmark datasets.
  arXiv  Detail & Related papers  (2023-07-07T08:19:40Z)
• Regularization Through Simultaneous Learning: A Case Study on Plant Classification [0.0]
  This paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function. Remarkably, our approach demonstrates superior performance over models without regularization.
  arXiv  Detail & Related papers  (2023-05-22T19:44:57Z)
• Learning Transferable Adversarial Robust Representations via Multi-view Consistency [57.73073964318167]
  We propose a novel meta-adversarial multi-view representation learning framework with dual encoders. We demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains.
  arXiv  Detail & Related papers  (2022-10-19T11:48:01Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder [18.375585982984845]
  We focus on enhancing the model's ability to defend gradient-based adversarial attack during the model's training process. We propose two novel adversarial training approaches: CARL and RAR. Experiments show that the proposed two approaches outperform strong baselines on various text classification datasets.
  arXiv  Detail & Related papers  (2021-09-14T09:08:58Z)
• Delving into Data: Effectively Substitute Training for Black-box Attack [84.85798059317963]
  We propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
  arXiv  Detail & Related papers  (2021-04-26T07:26:29Z)

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.