In this work, we tackle the problem of open-ended learning by introducing a
method that simultaneously evolves agents and increasingly challenging
environments. Unlike previous open-ended approaches that optimize agents using
a fixed neural network topology, we hypothesize that generalization can be
improved by allowing agents' controllers to become more complex as they
encounter more difficult environments. Our method, Augmentative Topology EPOET
(ATEP), extends the Enhanced Paired Open-Ended Trailblazer (EPOET) algorithm by
allowing agents to evolve their own neural network structures over time, adding
complexity and capacity as necessary. Empirical results demonstrate that ATEP
results in general agents capable of solving more environments than a
fixed-topology baseline. We also investigate mechanisms for transferring agents
between environments and find that a species-based approach further improves
the performance and generalization of agents.

本文提出了一种名为 ATEP 的增强型拓扑 EPOET 算法来同时进化越来越具有挑战性的环境和智能体的控制器结构，并证明这种方法相比于固定神经网络结构的基线算法具有更强的泛化性能，同时采用基于物种的转移机制有利于进一步提升智能体的表现和泛化能力。

开放式学习的增强拓扑智能体

Augmentative Topology Agents For Open-Ended Learning

We study the problem of recognizing visual entities from the textual
descriptions of their classes. Specifically, given birds' images with free-text
descriptions of their species, we learn to classify images of previously-unseen
species based on specie descriptions. This setup has been studied in the vision
community under the name zero-shot learning from text, focusing on learning to
transfer knowledge about visual aspects of birds from seen classes to
previously-unseen ones. Here, we suggest focusing on the textual description
and distilling from the description the most relevant information to
effectively match visual features to the parts of the text that discuss them.
Specifically, (1) we propose to leverage the similarity between species,
reflected in the similarity between text descriptions of the species. (2) we
derive visual summaries of the texts, i.e., extractive summaries that focus on
the visual features that tend to be reflected in images. We propose a simple
attention-based model augmented with the similarity and visual summaries
components. Our empirical results consistently and significantly outperform the
state-of-the-art on the largest benchmarks for text-based zero-shot learning,
illustrating the critical importance of texts for zero-shot image-recognition.

本研究关注零样本学习的视觉问题，并提出了使用基于文本描述的分类器，该分类器关注从图像描述中提取最相关的信息并将视觉特征与文本进行匹配，通过基于关注机制的简单模型，利用物种之间的相似度和文本的可视摘要来提高零样本视觉对象识别的效果。

ZEST: 从文本描述中使用文本相似度和视觉摘要实现零样本学习

ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity  and Visual Summarization

Existing image classification datasets used in computer vision tend to have a
uniform distribution of images across object categories. In contrast, the
natural world is heavily imbalanced, as some species are more abundant and
easier to photograph than others. To encourage further progress in challenging
real world conditions we present the iNaturalist species classification and
detection dataset, consisting of 859,000 images from over 5,000 different
species of plants and animals. It features visually similar species, captured
in a wide variety of situations, from all over the world. Images were collected
with different camera types, have varying image quality, feature a large class
imbalance, and have been verified by multiple citizen scientists. We discuss
the collection of the dataset and present extensive baseline experiments using
state-of-the-art computer vision classification and detection models. Results
show that current non-ensemble based methods achieve only 67% top one
classification accuracy, illustrating the difficulty of the dataset.
Specifically, we observe poor results for classes with small numbers of
training examples suggesting more attention is needed in low-shot learning.

本论文提出了 iNaturalist 物种分类和检测数据集，包括来自全球 5000 多种植物和动物的 859,000 张图片，其中包括视觉相似的物种以及具有各种图像质量和类不平衡性的图片。通过对数据集进行广泛的基准实验，本文的结果表明目前基于非集成方法的计算机视觉分类和检测模型只能达到 67% 的准确率，提出了面临的挑战和需要更多低阶学习关注的解决方案。