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08/22/2023
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning
Class-Incremental Learning (CIL) aims to build classification models fro...
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01/20/2023
Open-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
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09/13/2022
Test-Time Adaptation with Principal Component Analysis
Machine Learning models are prone to fail when test data are different f...
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06/18/2022
Model-Agnostic Few-Shot Open-Set Recognition
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
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05/10/2022
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Every day, a new method is published to tackle Few-Shot Image Classifica...
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02/01/2022
Minority Class Oriented Active Learning for Imbalanced Datasets
Active learning aims to optimize the dataset annotation process when res...
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02/01/2022
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Deep learning approaches are successful in a wide range of AI problems a...
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01/18/2022
Optimizing Active Learning for Low Annotation Budgets
When we can not assume a large amount of annotated data , active learnin...
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11/09/2021
Improving Next-Application Prediction with Deep Personalized-Attention Neural Network
Recently, due to the ubiquity and supremacy of E-recruitment platforms, ...
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10/15/2021
StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data
This paper tackles the problem of processing and combining efficiently a...
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08/30/2021
Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding
Drug repurposing is more relevant than ever due to drug development's ri...
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06/21/2021
Leveraging Conditional Generative Models in a General Explanation Framework of Classifier Decisions
Providing a human-understandable explanation of classifiers' decisions h...
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05/25/2021
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
Few-Shot Learning (FSL) algorithms have made substantial progress in lea...
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12/26/2020
Spatial Contrastive Learning for Few-Shot Classification
Existing few-shot classification methods rely to some degree on the cros...
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12/21/2020
AVAE: Adversarial Variational Auto Encoder
Among the wide variety of image generative models, two models stand out:...
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12/14/2020
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification
Explaining decisions of black-box classifiers is paramount in sensitive ...
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12/03/2020
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a so...
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07/16/2020
Autoregressive Unsupervised Image Segmentation
In this work, we propose a new unsupervised image segmentation approach ...
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06/25/2020
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Learning Invariant Representations has been successfully applied for rec...
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06/24/2020
Robust Domain Adaptation: Representations, Weights and Inductive Bias
Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in...
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06/09/2020
An Overview of Deep Semi-Supervised Learning
Deep neural networks demonstrated their ability to provide remarkable pe...
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03/19/2020
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
In this paper, we present a novel cross-consistency based semi-supervise...
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01/28/2020
Controlling generative models with continuous factors of variations
Recent deep generative models are able to provide photo-realistic images...
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01/13/2020
Représentations lexicales pour la détection non supervisée d'événements dans un flux de tweets : étude sur des corpus français et anglais
In this work, we evaluate the performance of recent text embeddings for ...
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12/30/2019
A New Approach for Explainable Multiple Organ Annotation with Few Data
Despite the recent successes of deep learning, such models are still far...
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07/29/2019
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets
Learning representations which remain invariant to a nuisance factor has...
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07/29/2019
Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation
Unsupervised Domain Adaptation aims to learn a model on a source domain ...
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10/04/2018
Learning Finer-class Networks for Universal Representations
Many real-world visual recognition use-cases can not directly benefit fr...
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12/27/2017
Learning More Universal Representations for Transfer-Learning
Transfer learning is commonly used to address the problem of the prohibi...
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02/26/2015
Relaxation-based revision operators in description logics
As ontologies and description logics (DLs) reach out to a broader audien...
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04/18/2013
