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Methods/Label Smoothing

Label Smoothing

GeneralIntroduced 198514332 papers

Description

Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\log{p}\left(y\mid{x}\right)$ directly can be harmful. Assume for a small constant $\epsilon$ , the training set label $y$ is correct with probability $1-\epsilon$ and incorrect otherwise. Label Smoothing regularizes a model based on a softmax with $k$ output values by replacing the hard $0$ and $1$ classification targets with targets of $\frac{\epsilon}{k}$ and $1-\frac{k-1}{k}\epsilon$ respectively.

Source: Deep Learning, Goodfellow et al

Image Source: When Does Label Smoothing Help?

Papers Using This Method

DASViT: Differentiable Architecture Search for Vision Transformer2025-07-17 Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows2025-07-16 DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition2025-07-16 Langevin Flows for Modeling Neural Latent Dynamics2025-07-15 Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures2025-07-15 KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding2025-07-15 Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking2025-07-15 Token Compression Meets Compact Vision Transformers: A Survey and Comparative Evaluation for Edge AI2025-07-13 Learning from Synthetic Labs: Language Models as Auction Participants2025-07-12 Comparative Analysis of Vision Transformers and Traditional Deep Learning Approaches for Automated Pneumonia Detection in Chest X-Rays2025-07-11 Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving2025-07-08 Geo-Registration of Terrestrial LiDAR Point Clouds with Satellite Images without GNSS2025-07-08 Tile-Based ViT Inference with Visual-Cluster Priors for Zero-Shot Multi-Species Plant Identification2025-07-08 A Wireless Foundation Model for Multi-Task Prediction2025-07-08 Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate2025-07-08 SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model2025-07-07 Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations2025-07-07 Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning2025-07-07 AI Generated Text Detection Using Instruction Fine-tuned Large Language and Transformer-Based Models2025-07-07 Fast and Simplex: 2-Simplicial Attention in Triton2025-07-03