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

• URL: http://arxiv.org/abs/2307.16019v1
• Date: Sat, 29 Jul 2023 16:21:40 GMT
• Title: Fuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual features matching
• Authors: Francesco Manigrasso and Lia Morra and Fabrizio Lamberti
• Abstract summary: 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. It achieves competitive performance to recent ZSL methods with less computational overhead.
• Score: 6.128849673587451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://gitlab.com/grains2/flvn.

Related papers

• Half-Space Feature Learning in Neural Networks [2.3249139042158853]
  There currently exist two extreme viewpoints for neural network feature learning. We argue neither interpretation is likely to be correct based on a novel viewpoint. We use this alternate interpretation to motivate a model, called the Deep Linearly Gated Network (DLGN)
  arXiv  Detail & Related papers  (2024-04-05T12:03:19Z)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Neuro-symbolic Rule Learning in Real-world Classification Tasks [75.0907310059298]
  We extend pix2rule's neural DNF module to support rule learning in real-world multi-class and multi-label classification tasks. We propose a novel extended model called neural DNF-EO (Exactly One) which enforces mutual exclusivity in multi-class classification.
  arXiv  Detail & Related papers  (2023-03-29T13:27:14Z)
• Two-level Graph Network for Few-Shot Class-Incremental Learning [7.815043173207539]
  Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points. Existing FSCIL methods ignore the semantic relationships between sample-level and class-level. In this paper, we designed a two-level graph network for FSCIL named Sample-level and Class-level Graph Neural Network (SCGN)
  arXiv  Detail & Related papers  (2023-03-24T08:58:08Z)
• Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification [13.521272923545409]
  Spi Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. This paper presents a heterogeneous spiking neural network (HRSNN) with unsupervised learning for video recognition tasks. We show that HRSNN can achieve similar performance to state-of-the-temporal backpropagation trained supervised SNN, but with less computation.
  arXiv  Detail & Related papers  (2022-09-22T16:34:01Z)
• PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning [2.236663830879273]
  Logic Networks (LTNs) are neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. We focus here on the subsumption or textttisOfClass predicate, which is fundamental to encode most semantic image interpretation tasks. We propose a common textttisOfClass predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype.
  arXiv  Detail & Related papers  (2022-06-26T18:34:07Z)
• Knowledge Enhanced Neural Networks for relational domains [83.9217787335878]
  We focus on a specific method, KENN, a Neural-Symbolic architecture that injects prior logical knowledge into a neural network. In this paper, we propose an extension of KENN for relational data.
  arXiv  Detail & Related papers  (2022-05-31T13:00:34Z)
• On Feature Learning in Neural Networks with Global Convergence Guarantees [49.870593940818715]
  We study the optimization of wide neural networks (NNs) via gradient flow (GF) We show that when the input dimension is no less than the size of the training set, the training loss converges to zero at a linear rate under GF. We also show empirically that, unlike in the Neural Tangent Kernel (NTK) regime, our multi-layer model exhibits feature learning and can achieve better generalization performance than its NTK counterpart.
  arXiv  Detail & Related papers  (2022-04-22T15:56:43Z)
• Isometric Propagation Network for Generalized Zero-shot Learning [72.02404519815663]
  A popular strategy is to learn a mapping between the semantic space of class attributes and the visual space of images based on the seen classes and their data. We propose Isometric propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces. IPN achieves state-of-the-art performance on three popular Zero-shot learning benchmarks.
  arXiv  Detail & Related papers  (2021-02-03T12:45:38Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.