Supervised Topological Maps
- URL: http://arxiv.org/abs/2008.06395v3
- Date: Wed, 2 Sep 2020 09:30:22 GMT
- Title: Supervised Topological Maps
- Authors: Francesco Mannella
- Abstract summary: Controlling the internal representation space of a neural network is a desirable feature because it allows to generate new data in a supervised manner.
We will show how this can be achieved while building a low-dimensional mapping of the input stream, by deriving a generalized algorithm starting from Self Organizing Maps (SOMs)
- Score: 0.76146285961466
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Controlling the internal representation space of a neural network is a
desirable feature because it allows to generate new data in a supervised
manner. In this paper we will show how this can be achieved while building a
low-dimensional mapping of the input stream, by deriving a generalized
algorithm starting from Self Organizing Maps (SOMs). SOMs are a kind of neural
network which can be trained with unsupervised learning to produce a
low-dimensional discretized mapping of the input space. They can be used for
the generation of new data through backward propagation of interpolations made
from the mapping grid. Unfortunately the final topology of the mapping space of
a SOM is not known before learning, so interpolating new data in a supervised
way is not an easy task. Here we will show a variation from the SOM algorithm
consisting in constraining the update of prototypes so that it is also a
function of the distance of its prototypes from extrinsically given targets in
the mapping space. We will demonstrate how such variants, that we will call
Supervised Topological Maps (STMs), allow for a supervised mapping where the
position of internal representations in the mapping space is determined by the
experimenter. Controlling the internal representation space in STMs reveals to
be an easier task than what is currently done using other algorithms such as
variational or adversarial autoencoders.
Related papers
- A rank decomposition for the topological classification of neural representations [0.0]
In this work, we leverage the fact that neural networks are equivalent to continuous piecewise-affine maps.
We study the homology groups of the quotient of a manifold $mathcalM$ and a subset $A$, assuming some minimal properties on these spaces.
We show that in randomly narrow networks, there will be regions in which the (co)homology groups of a data manifold can change.
arXiv Detail & Related papers (2024-04-30T17:01:20Z) - Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
We propose a transformer-based deep homography estimation (DHE) network.
It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification.
Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
arXiv Detail & Related papers (2024-02-25T13:22:17Z) - Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building [29.630483662400444]
We propose and apply the concept of Fragmentation-and-Recall (FARMap) in the mapping of large spaces.
Agents solve the mapping problem by building local maps via a surprisal-based clustering of space.
We demonstrate that FARMap replicates the fragmentation points observed in animal studies.
arXiv Detail & Related papers (2023-07-11T20:40:19Z) - highway2vec -- representing OpenStreetMap microregions with respect to
their road network characteristics [3.5960954499553512]
We propose a method for generating microregions' embeddings with respect to road infrastructure characteristics.
We base our representations on OpenStreetMap road networks in a selection of cities.
We obtained vector representations that detect how similar map hexagons are in the road networks they contain.
arXiv Detail & Related papers (2023-04-26T23:16:18Z) - Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations.
We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions.
Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
arXiv Detail & Related papers (2022-06-17T09:40:14Z) - CHALLENGER: Training with Attribution Maps [63.736435657236505]
We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance.
In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
arXiv Detail & Related papers (2022-05-30T13:34:46Z) - iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
iSDF is a continuous learning system for real-time signed distance field reconstruction.
It produces more accurate reconstructions and better approximations of collision costs and gradients.
arXiv Detail & Related papers (2022-04-05T15:48:39Z) - A singular Riemannian geometry approach to Deep Neural Networks II.
Reconstruction of 1-D equivalence classes [78.120734120667]
We build the preimage of a point in the output manifold in the input space.
We focus for simplicity on the case of neural networks maps from n-dimensional real spaces to (n - 1)-dimensional real spaces.
arXiv Detail & Related papers (2021-12-17T11:47:45Z) - Dendritic Self-Organizing Maps for Continual Learning [0.0]
We propose a novel algorithm inspired by biological neurons, termed Dendritic-Self-Organizing Map (DendSOM)
DendSOM consists of a single layer of SOMs, which extract patterns from specific regions of the input space.
It outperforms classical SOMs and several state-of-the-art continual learning algorithms on benchmark datasets.
arXiv Detail & Related papers (2021-10-18T14:47:19Z) - MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data.
We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.
To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
arXiv Detail & Related papers (2021-07-22T03:10:51Z) - A Boundary Regression Model for Nested Named Entity Recognition [17.968819067122418]
Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for an NE candidate in a sentence.
Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations.
In this paper, the regression operation is introduced to locate NEs in a sentence.
arXiv Detail & Related papers (2020-11-29T10:04:38Z)
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.