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Found 20 papers, 6 papers with code
Can Masked Autoencoders Also Listen to Birds?
Masked Autoencoders (MAEs) pretrained on AudioSet fail to capture the fine-grained acoustic characteristics of specialized domains such as bioacoustic monitoring.
From underwater to aerial: a novel multi-scale knowledge distillation approach for coral reef monitoring
This study presents a novel multi-scale approach to coral reef monitoring, integrating fine-scale underwater imagery with medium-scale aerial imagery.
Applying the maximum entropy principle to neural networks enhances multi-species distribution models
Arbitrarily complex transformations of input variables can be learned from the data efficiently through backpropagation and stochastic gradient descent (SGD).
MALPOLON: A Framework for Deep Species Distribution Modeling
This paper describes a deep-SDM framework, MALPOLON.
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem.
GeoPlant: Spatial Plant Species Prediction Dataset
The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts.
Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?
This strategy estimates user expertise as a trust score per user based on their ability to identify plant species from crowdsourced data.
Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
Cross-validation yields average accuracies of 0. 61 for status classification and 0. 78 for binary classification.
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island.
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability.
A two-head loss function for deep Average-K classification
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples.
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
We adapt the AUM to identify ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted Areas Under the Margin (WAUM).
How does the degree of novelty impacts semi-supervised representation learning for novel class retrieval?
We hypothesize that incorporating unlabelled images of novel classes in the training set in a semi-supervised fashion would be beneficial for the efficient retrieval of novel-class images compared to a vanilla supervised representation.
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice.
Classification Under Ambiguity: When Is Average-K Better Than Top-K?
This paper formally characterizes the ambiguity profile when average-$K$ classification can achieve a lower error rate than a fixed top-$K$ classification.
Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum
We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum.
Hyperspherical embedding for novel class classification
However, this traditional approach is not useful for identifying classes unseen on the training set, known as the open set problem.
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm.
Evaluation of Deep Species Distribution Models using Environment and Co-occurrences
This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods.
