Does label smoothing mitigate label noise?
Jul 12, 2020
Speakers
Srinadh Bhojanapalli
Speaker · 0 followers
Aditya Menon
Speaker · 3 followers
Sanjiv Kumar
Speaker · 2 followers
Michal Lukasik
Speaker · 0 followers
About
Label smoothing is commonly used in training deep learning models, wherein one-hot training labels are mixed with uniform label vectors. Empirically, smoothing has been shown to improve both predictive performance and model calibration. In this paper, we study whether label smoothing is also effective as a means of coping with label noise. While label smoothing apparently amplifies this problem — being equivalent to injecting symmetric noise to the labels — we show how it relates to a general family of loss-correction techniques from the label noise literature. Building on this connection, we show that label smoothing can be competitive with loss-correction techniques under label noise. Further, we show that when performing distillation under label noise, label smoothing of the teacher can be beneficial; this is in contrast to recent findings for noise-free problems, and sheds further light on settings where label smoothing is beneficial.Label smoothing is commonly used in training deep learning models, wherein one-hot training labels are mixed with uniform label vectors. Empirically, smoothing has been shown to improve both predictive performance and model calibration. In this paper, we study whether label smoothing is also effective as a means of coping with label noise. While label smoothing apparently amplifies this problem — being equivalent to injecting symmetric noise to the labels — we show how it relates to a general fa…
Categories
AI & Data Science
Category · 10.8k presentations
About ICML 2020
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
PowerNorm: Rethinking Batch Normalization in Transformers
Watch later
Sheng Shen, …
Word Embeddings for Chemical Patent Natural Language Processing
Watch later
Adversarial Nonnegative Matrix Factorization
Watch later
Lei Luo, …
Learning What to Defer for Maximum Independent Sets
Watch later
Sungsoo Ahn, …
