All language subtitles for 004什么是迁移学习

What is transfer learning?

The idea of Transfer Learning is to leverage the
knowledge acquired by a model trained with lots of

data on another task. The model A will be trained
specifically for task A. Now, let's say you want

to train a model B for a different task. One
option would be to train the model from scratch.

This could take lots of computation, time and
data. Instead, we could initialize model B with

the same weights as model A, transferring
the knowledge of model A on task B.

When training from scratch, all the model’s
weight are initialized randomly. In this example,

we are training a BERT model on the task of
recognizing if two sentences are similar or not.

On the left, it’s trained from scratch, and
on the right, it’s fine-tuning a pretrained

model. As we can see, using transfer learning
and the pretrained model yields better results.

And it doesn’t matter if we train longer, the
training from scratch is capped around 70%

accuracy while the pretrained
model beats the 86% easily.

This is because pretrained models are usually
trained on large amounts of data that provide

the model with a statistical understanding
of the language used during pretraining.

In computer vision, transfer learning has been
applied successfully for almost ten years.

Models are frequently pretrained on ImageNet, a
dataset containing 1.2 millions of photo images.

Each image is classified by one of
1000 labels. Training like this,

on labeled data is called supervised
learning. In Natural Language Processing,

transfer learning is a bit more recent. A key
difference with ImageNet is that the pretraining

is usually self-supervised, which means it
doesn’t require humans annotations for the labels.

A very common pretraining objective is
to guess the next word in a sentence,

which only requires lots and
lots of text. GPT-2 for instance,

was pretrained this way using the content of 45
millions links posted by users on Reddit. Another

example of self-supervised pretraining objective
is to predict the value of randomly masked words,

which is similar to fill-in-the-blank
tests you may have done in school.

BERT was pretrained this way using the English
Wikipedia and 11,000 unpublished books.

In practice, transfer learning is applied on a
given model by throwing away its head, that is,

its last layers focused on the pretraining
objective, and replacing it with a new,

randomly initialized, head suitable for the task
at hand. For instance, when we fine-tuned a BERT

model earlier, we removed the head that classified
mask words and replaced it with a classifier with

2 outputs, since our task had two labels. To
be as efficient as possible, the pretrained

model used should be as similar as possible
to the task it’s fine-tuned on. For instance,

if the problem it’s to classify German sentences,
it’s best to use a German pretrained model.

But with the good comes the bad. The pretrained
model does not only transfer its knowledge,

but also any bias it may contain. ImageNet mostly
contains images coming from the United States and

Western Europe, so models fine-tuned with it
usually will perform better on images from

these countries. OpenAI also studied the
bias in the predictions of its GPT-3 model

(which was pretrained using the guess the next
work objective). Changing the gender of the prompt

from "He was very" to "She was very" changed
the predictions from mostly neutral adjectives

to almost only physical ones. In
their model card of the GPT-2 model,

OpenAI also acknowledges its bias and discourages
its use in systems that interact with humans.