"A Data-Based Perspective on Transfer Learning"

Different classes in a pretraining dataset can have different effects on downstream accuracy. And you can use this to your advantage. [1/9]

They assess these effects using a simple algorithm that trains different models on different subsets of the data and looks at both the class counts and the predictions for each model on each downstream sample. [2/9]
Using their scoring function, you can intelligently remove subsets of classes from the pretraining dataset in order to significantly raise downstream accuracy. [3/9]
Another use of their method is identifying more granular subpopulations than what a downstream task has annotated. E.g., you can find which CIFAR-10 images look most like ostriches even though CIFAR-10 only has the label “bird”. [4/9]
You can also use a similar idea to understand model failure modes or identify data leakage. [5/9]
And last but not least, you can use it to understand helpful/harmful samples in your pretraining dataset. [6/9]
Overall their algorithm seems like a great tool to have in the toolbox. [7/9]
Paper: https://t.co/CKg0nxmSxE

If you like this paper, consider RTing this (or another!) thread to publicize the authors' work, or following the authors: @saachi_jain_ @hadisalmanX @Alaa_Khaddaj… [8/9]
@saachi_jain_ @hadisalmanX @Alaa_Khaddaj …@RICEric22 @ssung_mminn @aleks_madry

For more paper summaries, you might like following @mosaicml, me, or my newsletter: https://t.co/5BMBC84xY8

As always, comments and corrections welcome! [9/9] https://t.co/8VRLAGmrfQ

More from All

You May Also Like

"I lied about my basic beliefs in order to keep a prestigious job. Now that it will be zero-cost to me, I have a few things to say."


We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.

Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)

It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.

Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".