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Found 28 papers, 4 papers with code
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations
Contrastive instance discrimination outperforms supervised learning in downstream tasks like image classification and object detection.
Masked Capsule Autoencoders
We propose Masked Capsule Autoencoders (MCAE), the first Capsule Network that utilises pretraining in a self-supervised manner.
ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method
Our findings underscore the potential of our proposed methodology in enhancing the operational efficiency and performance of Capsule Networks, paving the way for their application in increasingly complex computational scenarios.
Tabular Machine Learning Methods for Predicting Gas Turbine Emissions
Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere.
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods
Self-supervised learning algorithms (SSL) based on instance discrimination have shown promising results, performing competitively or even outperforming supervised learning counterparts in some downstream tasks.
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning
In this work, we aim to learn highly separable semantic hierarchical representations by stacking Joint Embedding Architectures (JEA) where higher-level JEAs are input with representations of lower-level JEA.
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes
Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion within a low-dimensional representation space.
Vanishing Activations: A Symptom of Deep Capsule Networks
This paper extends the investigation to a range of leading Capsule Network architectures, demonstrating that these issues are not confined to the original design.
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing
Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos.
Deep learning universal crater detection using Segment Anything Model (SAM)
Existing ML approaches for automated crater detection have been trained in specific types of data e. g. digital elevation model (DEM), images and associated metadata for orbiters such as the Lunar Reconnaissance Orbiter Camera (LROC) etc.. Due to that, each of the resulting ML schemes is applicable and reliable only to the type of data used during the training process.
Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting
Yield forecasting is a critical first step necessary for yield optimisation, with important consequences for the broader food supply chain, procurement, price-negotiation, logistics, and supply.
LLEDA -- Lifelong Self-Supervised Domain Adaptation
Humans and animals have the ability to continuously learn new information over their lifetime without losing previously acquired knowledge.
Learning with Capsules: A Survey
The aim of this survey is to provide a comprehensive overview of the capsule network research landscape, which will serve as a valuable resource for the community going forward.
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting
We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE compatible neural networks with our own open-source framework and reproducible examples.
Fully Homomorphically Encrypted Deep Learning as a Service
Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies.
Hyperspherically Regularized Networks for Self-Supervision
Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative sampling associated with such methods.
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector.
Contrastive Domain Adaptation
Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks.
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth
Finally, a recurrent neural network including LSTM and an attention mechanism is proposed for modelling long-term dependencies in the time series data.
Introducing Routing Uncertainty in Capsule Networks
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment.
A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms
This paper proposes an enhanced natural language generation system combining the merits of both rule-based approaches and modern deep learning algorithms, boosting its performance to the extent where the generated textual content is capable of exhibiting agile human-writing styles and the content logic of which is highly controllable.
Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach
Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the duration of rain vs non-rain.
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks.
Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments
Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general.
Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing.
Capsule Routing via Variational Bayes
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks.
Ranked #3 on
Image Classification
on smallNORB
Deep Bayesian Self-Training
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks.
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.
