The human cognitive system exhibits remarkable flexibility and generalization
capabilities, partly due to its ability to form low-dimensional, compositional
representations of the environment. In contrast, standard neural network
architectures often struggle with abstract reasoning tasks, overfitting, and
requiring extensive data for training. This paper investigates the impact of
the relational bottleneck -- a mechanism that focuses processing on relations
among inputs -- on the learning of factorized representations conducive to
compositional coding and the attendant flexibility of processing. We
demonstrate that such a bottleneck not only improves generalization and
learning efficiency, but also aligns network performance with human-like
behavioral biases. Networks trained with the relational bottleneck developed
orthogonal representations of feature dimensions latent in the dataset,
reflecting the factorized structure thought to underlie human cognitive
flexibility. Moreover, the relational network mimics human biases towards
regularity without pre-specified symbolic primitives, suggesting that the
bottleneck fosters the emergence of abstract representations that confer
flexibility akin to symbols.

研究关系瓶颈对因式表示学习和处理灵活性的影响，并证明该瓶颈不仅改善了泛化和学习效率，而且与人类行为偏差相一致，从而促进了抽象表示的产生，使处理灵活性类似于符号。

神经网络中的维度抽象的关系归纳偏好

A Relational Inductive Bias for Dimensional Abstraction in Neural  Networks

Biological vision systems make adaptive use of context to recognize objects
in new settings with novel contexts as well as occluded or blurry objects in
familiar settings. In this paper, we investigate how vision models adaptively
use context for out-of-distribution (OOD) generalization and leverage our
analysis results to improve model OOD generalization. First, we formulate two
distinct OOD settings where the contexts are either irrelevant
(Background-Invariance) or beneficial (Object-Disambiguation), reflecting the
diverse contextual challenges faced in biological vision. We then analyze model
performance in these two different OOD settings and demonstrate that models
that excel in one setting tend to struggle in the other. Notably, prior works
on learning causal features improve on one setting but hurt in the other. This
underscores the importance of generalizing across both OOD settings, as this
ability is crucial for both human cognition and robust AI systems. Next, to
better understand the model properties contributing to OOD generalization, we
use representational geometry analysis and our own probing methods to examine a
population of models, and we discover that those with more factorized
representations and appropriate feature weighting are more successful in
handling Background-Invariance and Object-Disambiguation tests. We further
validate these findings through causal intervention on representation
factorization and feature weighting to demonstrate their causal effect on
performance. Lastly, we propose new augmentation methods to enhance model
generalization. These methods outperform strong baselines, yielding
improvements in both in-distribution and OOD tests. In conclusion, to replicate
the generalization abilities of biological vision, computer vision models must
have factorized object vs. background representations and appropriately weight
both kinds of features.

本文探讨了视觉模型如何自适应地利用上下文进行分布外泛化，研究发现，具有因子化的表征和适当特征加权的模型可以更好地处理背景不变性和物体消歧，提出了新的数据扩增方法，提高了模型的泛化能力。

自适应情境感知：如何推广应用于新的背景和具有模糊性的物体

Adaptive Contextual Perception: How to Generalize to New Backgrounds and  Ambiguous Objects

Humans and animals show remarkable flexibility in adjusting their behaviour
when their goals, or rewards in the environment change. While such flexibility
is a hallmark of intelligent behaviour, these multi-task scenarios remain an
important challenge for machine learning algorithms and neurobiological models
alike. We investigated two approaches that could enable this flexibility:
factorized representations, which abstract away general aspects of a task from
those prone to change, and nonparametric, memory-based approaches, which can
provide a principled way of using similarity to past experiences to guide
current behaviour. In particular, we combine the successor representation (SR)
that factors the value of actions into expected outcomes and corresponding
rewards with evaluating task similarity through clustering the space of reward
functions. The proposed algorithm inverts a generative model over tasks, and
dynamically samples from a flexible number of distinct SR maps while
accumulating evidence about the current task context through amortized
inference. It improves SR's transfer capabilities and outperforms competing
algorithms and baselines in settings with both known and unsignalled rewards
changes. Further, as a neurobiological model of spatial coding in the
hippocampus, it explains important signatures of this representation, such as
the "flickering" behaviour of hippocampal maps, and trajectory-dependent place
cells (so-called splitter cells) and their dynamics. We thus provide a novel
algorithmic approach for multi-task learning, as well as a common normative
framework that links together these different characteristics of the brain's
spatial representation.

我们提出了一种算法来解决多任务学习中的转移问题，该算法结合了因子化表示和非参数、基于记忆的方法，并利用后继表示法来提高转移能力和性能表现。同时，该算法提供了一个更好的神经生物学模型来解释海马体区域的空间编码特征。