test distribution
Clip_Dataset__NeurIPS2022_ (10)
We find that the performance of the pre-training data varies substantially across distribution shifts, with no single data source dominating.
Technology:
CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
Adapting to dynamic data distributions is a practical yet challenging task.
Country:
- North America > United States > Michigan > Washtenaw County > Ann Arbor (0.04)
- North America > United States > California > Riverside County > Riverside (0.04)
- Europe > France > Grand Est > Bas-Rhin > Strasbourg (0.04)
- Asia > Middle East > Israel (0.04)
Genre:
- Research Report > New Finding (1.00)
- Research Report > Experimental Study (1.00)
Industry: Government > Regional Government > North America Government > United States Government (0.46)
Technology:
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Sensing and Signal Processing > Image Processing (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.67)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.46)
Country:
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > Louisiana > Orleans Parish > New Orleans (0.04)
- North America > United States > Utah > Salt Lake County > Salt Lake City (0.04)
- (5 more...)
Technology:
Country:
- North America > United States > Alaska (0.05)
- North America > United States > Pennsylvania (0.05)
- North America > United States > Texas (0.04)
- (5 more...)
Industry:
- Health & Medicine (1.00)
- Information Technology (0.68)
- Government > Regional Government > North America Government > United States Government (0.68)
- Government > Military (0.68)
Technology:
Generativemultitasklearningmitigatestarget-causing confounding
We refer to them astarget-causing confounders.
Country:
- North America > United States (0.14)
- Asia > Middle East > Jordan (0.04)
Technology:
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.93)
Country:
- Europe > Switzerland > Zürich > Zürich (0.14)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Germany (0.04)
Technology:
Country:
- Europe > Spain > Andalusia > Granada Province > Granada (0.04)
- Asia > Middle East > Jordan (0.04)
Industry:
- Information Technology (0.93)
- Health & Medicine > Therapeutic Area (0.46)
Technology:
Test-TimeTrainingwithMaskedAutoencoders
In this paper, we use masked autoencoders for this one-sample learning problem.
Country:
- North America > United States (0.04)
- Europe > Netherlands > South Holland > Delft (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.89)
