Imagen de Portada

Rethinking Dataset Distillation: Hard Truths about Soft Labels

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Dey, Priyam, Sahdev, Aditya, Bhati, Sunny, Mopuri, Konda Reddy, Babu, R. Venkatesh
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

DiRe: Diversity-promoting Regularization for Dataset Condensation
por: Mohanty, Saumyaranjan, et al.
Publicado: (2025)

ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations
por: Addepalli, Sravanti, et al.
Publicado: (2024)

Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
por: Lingao, Xiao, et al.
Publicado: (2026)

DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
por: Rangwani, Harsh, et al.
Publicado: (2024)

Dataset Distillers Are Good Label Denoisers In the Wild
por: Cheng, Lechao, et al.
Publicado: (2024)

A Label is Worth a Thousand Images in Dataset Distillation
por: Qin, Tian, et al.
Publicado: (2024)

Rethinking Large-scale Dataset Compression: Shifting Focus From Labels to Images
por: Xiao, Lingao, et al.
Publicado: (2025)

GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost
por: Shang, Xinyi, et al.
Publicado: (2024)

Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition
por: Adhikari, Sayanta, et al.
Publicado: (2024)

Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
por: Zhao, Shiji, et al.
Publicado: (2023)

Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation
por: Moon, Jaeho, et al.
Publicado: (2024)

Exploring the Impact of Dataset Bias on Dataset Distillation
por: Lu, Yao, et al.
Publicado: (2024)

HSMix: Hard and Soft Mixing Data Augmentation for Medical Image Segmentation
por: Sun, Danyang, et al.
Publicado: (2025)

Towards Trustworthy Dataset Distillation
por: Ma, Shijie, et al.
Publicado: (2023)

Rethinking Decoupled Knowledge Distillation: A Predictive Distribution Perspective
por: Zheng, Bowen, et al.
Publicado: (2025)

Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification
por: Gore, Aakash, et al.
Publicado: (2025)

Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
por: Lu, Yangdi, et al.
Publicado: (2024)

Multi-Modal Dataset Distillation in the Wild
por: Dang, Zhuohang, et al.
Publicado: (2025)

Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets
por: Gu, Xulin, et al.
Publicado: (2025)

Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attention
por: Gupta, Shreyam, et al.
Publicado: (2025)

Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings
por: Swaminathan, Arjhun, et al.
Publicado: (2025)

Improve Cross-Architecture Generalization on Dataset Distillation
por: Zhou, Binglin, et al.
Publicado: (2024)

Understanding Dataset Distillation via Spectral Filtering
por: Bo, Deyu, et al.
Publicado: (2025)

Multi-Label Knowledge Distillation
por: Yang, Penghui, et al.
Publicado: (2023)

On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
por: Sun, Peng, et al.
Publicado: (2023)

An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
por: Menabue, Martin, et al.
Publicado: (2024)

Rethinking Score Distillation as a Bridge Between Image Distributions
por: McAllister, David, et al.
Publicado: (2024)

Composing Parts for Expressive Object Generation
por: Rangwani, Harsh, et al.
Publicado: (2024)

Towards Adversarially Robust Dataset Distillation by Curvature Regularization
por: Xue, Eric, et al.
Publicado: (2024)

Low-Rank Similarity Mining for Multimodal Dataset Distillation
por: Xu, Yue, et al.
Publicado: (2024)

DataDAM: Efficient Dataset Distillation with Attention Matching
por: Sajedi, Ahmad, et al.
Publicado: (2023)

Spectral Gradient Surgery for Domain-Generalizable Dataset Distillation
por: Oh, Minyoung, et al.
Publicado: (2026)

CLImage: Human-Annotated Datasets for Complementary-Label Learning
por: Wang, Hsiu-Hsuan, et al.
Publicado: (2023)

Vector-Quantized Soft Label Compression for Dataset Distillation
por: Abbasi, Ali, et al.
Publicado: (2026)

From Fewer Samples to Fewer Bits: Reframing Dataset Distillation as Joint Optimization of Precision and Compactness
por: Dinh, My H., et al.
Publicado: (2026)

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
por: Parikh, Aditya, et al.
Publicado: (2026)

Federated Learning with Label-Masking Distillation
por: Lu, Jianghu, et al.
Publicado: (2024)

Hyperbolic Dataset Distillation
por: Li, Wenyuan, et al.
Publicado: (2025)

Dataset Distillation as Data Compression: A Rate-Utility Perspective
por: Bao, Youneng, et al.
Publicado: (2025)

Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation
por: Zhang, Xin, et al.
Publicado: (2025)