Neuro-symbolic integration aims at harnessing the power of symbolic knowledge
representation combined with the learning capabilities of deep neural networks.
In particular, Logic Tensor Networks (LTNs) allow to incorporate background
knowledge in the form of logical axioms by grounding a first order logic
language as differentiable operations between real tensors. Yet, few studies
have investigated the potential benefits of this approach to improve zero-shot
learning (ZSL) classification. In this study, we present the Fuzzy Logic Visual
Network (FLVN) that formulates the task of learning a visual-semantic embedding
space within a neuro-symbolic LTN framework. FLVN incorporates prior knowledge
in the form of class hierarchies (classes and macro-classes) along with robust
high-level inductive biases. The latter allow, for instance, to handle
exceptions in class-level attributes, and to enforce similarity between images
of the same class, preventing premature overfitting to seen classes and
improving overall performance. FLVN reaches state of the art performance on the
Generalized ZSL (GZSL) benchmarks AWA2 and CUB, improving by 1.3% and 3%,
respectively. Overall, it achieves competitive performance to recent ZSL
methods with less computational overhead. FLVN is available at
this https URL

神经符号集成与学习深度神经网络的能力相结合，通过逻辑张量网络（LTN）来整合逻辑公理的背景知识。本研究介绍了模糊逻辑视觉网络（FLVN），它在神经符号逻辑张量网络框架中制定了学习视觉语义嵌入空间任务，通过引入先前知识（类别层次结构和宏类别）和健壮的高级归纳偏差来提高零样本学习分类任务性能。FLVN 在 AWA2 和 CUB 的泛化零样本学习基准测试中取得了最先进的性能，并且相对于最近的零样本学习方法具有更少的计算开销。

模糊逻辑视觉网络（FLVN）：一种神经符号化的视觉特征匹配方法

Fuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual  features matching

Two things seem to be indisputable in the contemporary deep learning
discourse: 1. The categorical cross-entropy loss after softmax activation is
the method of choice for classification. 2. Training a CNN classifier from
scratch on small datasets does not work well. In contrast to this, we show that
the cosine loss function provides significantly better performance than
cross-entropy on datasets with only a handful of samples per class. For
example, the accuracy achieved on the CUB-200-2011 dataset without pre-training
is by 30% higher than with the cross-entropy loss. Further experiments on other
popular datasets confirm our findings. Moreover, we demonstrate that
integrating prior knowledge in the form of class hierarchies is straightforward
with the cosine loss and improves classification performance further.

本文研究了使用余弦相似度损失函数进行小数据集分类的问题，发现与分类交叉熵损失函数相比，余弦损失函数在只有极少标本的数据集上具有更高的准确性，在使用类别树的先前知识时可以进一步提高分类性能。

使用余弦损失函数对小数据集进行无预训练深度学习

Deep Learning on Small Datasets without Pre-Training using Cosine Loss

One of the most prominent problems in machine learning in the age of deep
learning is the availability of sufficiently large annotated datasets. For
specific domains, e.g. animal species, a long-tail distribution means that some
classes are observed and annotated insufficiently. Additional labels can be
prohibitively expensive, e.g. because domain experts need to be involved.
However, there is more information available that is to the best of our
knowledge not exploited accordingly. In this paper, we propose to make use of
preexisting class hierarchies like WordNet to integrate additional domain
knowledge into classification. We encode the properties of such a class
hierarchy into a probabilistic model. From there, we derive a novel label
encoding and a corresponding loss function. On the ImageNet and NABirds
datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy
over the baseline respectively. After less than a third of training time, it is
already able to match the baseline's fine-grained recognition performance. Both
results show that our suggested method is efficient and effective.

基于 WordNet 类层次结构的机器学习方法提高了 ImageNet 和 NABirds 数据集的分类准确度，并在较短的训练时间内成功匹配了基线模型的细粒度识别性能。