Generalized Category Discovery (GCD) is a pragmatic and challenging
open-world task, which endeavors to cluster unlabeled samples from both novel
and old classes, leveraging some labeled data of old classes. Given that
knowledge learned from old classes is not fully transferable to new classes,
and that novel categories are fully unlabeled, GCD inherently faces intractable
problems, including imbalanced classification performance and inconsistent
confidence between old and new classes, especially in the low-labeling regime.
Hence, some annotations of new classes are deemed necessary. However, labeling
new classes is extremely costly. To address this issue, we take the spirit of
active learning and propose a new setting called Active Generalized Category
Discovery (AGCD). The goal is to improve the performance of GCD by actively
selecting a limited amount of valuable samples for labeling from the oracle. To
solve this problem, we devise an adaptive sampling strategy, which jointly
considers novelty, informativeness and diversity to adaptively select novel
samples with proper uncertainty. However, owing to the varied orderings of
label indices caused by the clustering of novel classes, the queried labels are
not directly applicable to subsequent training. To overcome this issue, we
further propose a stable label mapping algorithm that transforms ground truth
labels to the label space of the classifier, thereby ensuring consistent
training across different active selection stages. Our method achieves
state-of-the-art performance on both generic and fine-grained datasets. Our
code is available at this https URL

提出了一种新的主动学习设置，名为主动广义类别发现（AGCD），旨在通过主动选择有限数量的有价值样本进行标注，从而改善广义类别发现的性能。

主动广义类别发现

Active Generalized Category Discovery

Evaluation is crucial in Information Retrieval. The development of models,
tools and methods has significantly benefited from the availability of reusable
test collections formed through a standardized and thoroughly tested
methodology, known as the Cranfield paradigm. Constructing these collections
requires obtaining relevance judgments for a pool of documents, retrieved by
systems participating in an evaluation task; thus involves immense human labor.
To alleviate this effort different methods for constructing collections have
been proposed in the literature, falling under two broad categories: (a)
sampling, and (b) active selection of documents. The former devises a smart
sampling strategy by choosing only a subset of documents to be assessed and
inferring evaluation measure on the basis of the obtained sample; the sampling
distribution is being fixed at the beginning of the process. The latter
recognizes that systems contributing documents to be judged vary in quality,
and actively selects documents from good systems. The quality of systems is
measured every time a new document is being judged. In this paper we seek to
solve the problem of large-scale retrieval evaluation combining the two
approaches. We devise an active sampling method that avoids the bias of the
active selection methods towards good systems, and at the same time reduces the
variance of the current sampling approaches by placing a distribution over
systems, which varies as judgments become available. We validate the proposed
method using TREC data and demonstrate the advantages of this new method
compared to past approaches.

本文提出一种结合两种方法 —— 抽样和主动选择文档 —— 的大规模信息检索评估方法，其通过向系统分配分布并在评估过程中修改来减少样本偏差，并使用 TREC 数据验证其优点。

大规模信息检索评估的活跃抽样

Active Sampling for Large-scale Information Retrieval Evaluation

We consider a setting for Inverse Reinforcement Learning (IRL) where the
learner is extended with the ability to actively select multiple environments,
observing an agent's behavior on each environment. We first demonstrate that if
the learner can experiment with any transition dynamics on some fixed set of
states and actions, then there exists an algorithm that reconstructs the
agent's reward function to the fullest extent theoretically possible, and that
requires only a small (logarithmic) number of experiments. We contrast this
result to what is known about IRL in single fixed environments, namely that the
true reward function is fundamentally unidentifiable. We then extend this
setting to the more realistic case where the learner may not select any
transition dynamic, but rather is restricted to some fixed set of environments
that it may try. We connect the problem of maximizing the information derived
from experiments to submodular function maximization and demonstrate that a
greedy algorithm is near optimal (up to logarithmic factors). Finally, we
empirically validate our algorithm on an environment inspired by behavioral
psychology.

考虑逆强化学习的设置，其中学习者扩展了主动选择多个环境的能力，从而观察代理在每个环境中的行为。我们首先展示了，如果学习者可以在一些固定的状态和行动集上尝试任何过渡动态，那么存在一种重建代理奖励函数的算法，其理论上可能性最大，并且仅需要少量 (对数级别) 的实验。接着，我们将这个设置扩展到更加现实的情况，即学习者可能无法选择任何转移动态，而是受到一些固定环境的限制。我们将实验中得到的信息最大化问题与次模函数最大化联系起来，并展示了贪心算法是近似最优的 (对数因子)。最后，我们在一个受行为心理学启发的环境中对我们的算法进行了实证验证。