Why did success on website images fail to ensure success on mobile uploads?
Question: Answer in one to three sentences using the distribution difference described in the cat-app example.
Sample answer: Mobile uploads had a different look from website images, including being lower resolution, blurrier, and poorly lit. Because the training and test data consisted of website images, performance on those sets did not ensure generalization to the mobile distribution of interest.
Key points:
- Mobile and website images came from different distributions.
- Website-based evaluation did not establish performance on mobile uploads.
Rubric: The answer must connect the visual differences in mobile images to the mismatch between the evaluated distribution and the target distribution.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Avoid Randomly Shuffling Mixed-Source Data into Dev/Test Sets
Include Some Target-Distribution Examples in Training Alongside Auxiliary Data
Down-Weighting Auxiliary Data from a Different Distribution
Training Dev Set
Error Table Across Two Data Distributions and Three Error Types
Data Mismatch Between Training and Dev Set Distributions
Limited Practical Scope of Domain Adaptation for Different Data Distributions
Domain Adaptation for Different Data Distributions
Website Images and Mobile Phone Pictures as a Distribution Mismatch Example
Random 70/30 Train/Test Split Can Fail Under Distribution Shift
Which data should define the dev and test sets for the cat-picture app?
Dev and test sets should represent the future data distribution of interest.
Complete the principle: Dev and test sets should reflect _____ data.
Match each cat-app data group with its appropriate role or property.
Order the decisions for building datasets when auxiliary and target data differ.
Explain why different training and evaluation distributions can be appropriate.
Diagnose the evaluation-set mistake in a mobile cat-classification app.
Why did success on website images fail to ensure success on mobile uploads?
Which dataset design best uses both target and auxiliary cat images?
Using extra internet images for training requires internet images in dev and test sets.