Novel class discovery (NCD) aims to infer novel categories in an unlabeled
dataset by leveraging prior knowledge of a labeled set comprising disjoint but
related classes. Given that most existing literature focuses primarily on
utilizing supervised knowledge from a labeled set at the methodology level,
this paper considers the question: Is supervised knowledge always helpful at
different levels of semantic relevance? To proceed, we first establish a novel
metric, so-called transfer flow, to measure the semantic similarity between
labeled/unlabeled datasets. To show the validity of the proposed metric, we
build up a large-scale benchmark with various degrees of semantic similarities
between labeled/unlabeled datasets on ImageNet by leveraging its hierarchical
class structure. The results based on the proposed benchmark show that the
proposed transfer flow is in line with the hierarchical class structure; and
that NCD performance is consistent with the semantic similarities (measured by
the proposed metric). Next, by using the proposed transfer flow, we conduct
various empirical experiments with different levels of semantic similarity,
yielding that supervised knowledge may hurt NCD performance. Specifically,
using supervised information from a low-similarity labeled set may lead to a
suboptimal result as compared to using pure self-supervised knowledge. These
results reveal the inadequacy of the existing NCD literature which usually
assumes that supervised knowledge is beneficial. Finally, we develop a
pseudo-version of the transfer flow as a practical reference to decide if
supervised knowledge should be used in NCD. Its effectiveness is supported by
our empirical studies, which show that the pseudo transfer flow (with or
without supervised knowledge) is consistent with the corresponding accuracy
based on various datasets. Code is released at
this https URL

本研究建立了一个称为 transfer flow 的新度量标准，通过构建一个各种语义相似度的大型基准测试，研究表明对于不同层次的语义相关性，使用纯自我监督知识可能比使用少相似标记集的监督信息更为优越，提出了偏转换流的虚拟版本作为决定是否在 NCD 中使用监督信息的实用参考。

监督知识可能会影响新类别发现性能

Supervised Knowledge May Hurt Novel Class Discovery Performance

Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.

本文提出了一种名为无样本哈希（ZSH）的新型哈希方案，可以将 “看不见” 类别的图像压缩成二进制码，并从 “可见” 类别的有限训练数据中学习哈希函数。通过将独立的数据标签投影到语义嵌入空间中，可以精确表征所有标签的语义关系，并且可以将可见有监督知识转移给不可见类别。同时，通过旋转嵌入空间来更适当地对齐嵌入语义和低级视觉特征空间，缓解语义差的影响。此外，我们进一步提出保留二进制码中的局部结构属性和离散性质的方法，并开发了一种有效的交替算法来解决 ZSH 模型。在各种真实数据集上进行的大量实验表明，与几种最先进的哈希方法相比，ZSH 具有卓越的零样本图像检索性能。