"The power of a generalization system follows directly from its biases"
(Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems --
but to what degree have we understood how their inductive bias influences model
decisions? We here attempt to disentangle the various aspects that determine
how a model decides. In particular, we ask: what makes one model decide
differently from another? In a meticulously controlled setting, we find that
(1.) irrespective of the network architecture or objective (e.g.
self-supervised, semi-supervised, vision transformers, recurrent models) all
models end up with a similar decision boundary. (2.) To understand these
findings, we analysed model decisions on the ImageNet validation set from epoch
to epoch and image by image. We find that the ImageNet validation set, among
others, suffers from dichotomous data difficulty (DDD): For the range of
investigated models and their accuracies, it is dominated by 46.0% "trivial"
and 11.5% "impossible" images (beyond label errors). Only 42.5% of the images
could possibly be responsible for the differences between two models' decision
boundaries. (3.) Only removing the "impossible" and "trivial" images allows us
to see pronounced differences between models. (4.) Humans are highly accurate
at predicting which images are "trivial" and "impossible" for CNNs (81.4%).
This implies that in future comparisons of brains, machines and behaviour, much
may be gained from investigating the decisive role of images and the
distribution of their difficulties.

本研究旨在探究 CNN 模型鉴别决策的归纳偏差如何影响模型，发现 ImageNet 验证集中存在着二分数据困难，只有将 “不可能” 和 “琐碎” 的图像删除后，我们才能看到模型之间的明显差异，此外，作者还表明在进行大脑、机器和行为比较时，研究图像及其困难度分布的决定性角色可以获得更多的启示。

无足轻重或不可能 -- 二分数据难度掩盖了模型的差异（在 ImageNet 及以外）

Trivial or impossible -- dichotomous data difficulty masks model  differences (on ImageNet and beyond)

In naturalistic learning problems, a model's input contains a wide range of
features, some useful for the task at hand, and others not. Of the useful
features, which ones does the model use? Of the task-irrelevant features, which
ones does the model represent? Answers to these questions are important for
understanding the basis of models' decisions, as well as for building models
that learn versatile, adaptable representations useful beyond the original
training task. We study these questions using synthetic datasets in which the
task-relevance of input features can be controlled directly. We find that when
two features redundantly predict the labels, the model preferentially
represents one, and its preference reflects what was most linearly decodable
from the untrained model. Over training, task-relevant features are enhanced,
and task-irrelevant features are partially suppressed. Interestingly, in some
cases, an easier, weakly predictive feature can suppress a more strongly
predictive, but more difficult one. Additionally, models trained to recognize
both easy and hard features learn representations most similar to models that
use only the easy feature. Further, easy features lead to more consistent
representations across model runs than do hard features. Finally, models have
greater representational similarity to an untrained model than to models
trained on a different task. Our results highlight the complex processes that
determine which features a model represents.

研究自然学习问题中的特征选择，发现训练后任务相关的特征增强，任务无关的特征部分抑制，容易的特征对模型具有更大的一致性和代表性，并且对未经训练的模型具有更大的相似性。