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Found 20 papers, 9 papers with code
Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
Online news consumption plays an important role in shaping the political opinions of citizens.
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes.
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning
Mainstream methods need to store two deep models since they integrate new classes using fine tuning with knowledge distillation from the previous incremental state.
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Since memory is bounded, old classes are learned with fewer images than new classes and an imbalance due to incremental learning is added to the initial dataset imbalance.
Minority Class Oriented Active Learning for Imbalanced Datasets
Here, we introduce a new active learning method which is designed for imbalanced datasets.
Optimizing Active Learning for Low Annotation Budgets
It consists in learning a model on a small amount of annotated data (annotation budget) and in choosing the best set of points to annotate in order to improve the previous model and gain in generalization.
Dataset Knowledge Transfer for Class-Incremental Learning without Memory
We introduce a two-step learning process which allows the transfer of bias correction parameters between reference and target datasets.
Face Verification with Challenging Imposters and Diversified Demographics
We introduce a series of design choices which address these challenges and make the dataset constitution and usage more sustainable and fairer.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
Unveiling Real-Life Effects of Online Photo Sharing
The approach relies on three components: (1) a set of visual objects with associated situation impact ratings obtained by crowdsourcing, (2) a corresponding set of object detectors for mining users' photos and (3) a ground truth dataset made of 500 visual user profiles which are manually rated per situation.
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model.
Initial Classifier Weights Replay for Memoryless Class Incremental Learning
It leverages initial classifier weights which provide a strong representation of past classes because they are trained with all class data.
Active Class Incremental Learning for Imbalanced Datasets
Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1) new data are assumed to be readily annotated when streamed and (2) tests are run with balanced datasets while most real-life datasets are actually imbalanced.
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning
When the number of classes is large, classes are usually represented by semantic class prototypes learned automatically from unannotated text collections.
ScaIL: Classifier Weights Scaling for Class Incremental Learning
The problem is non trivial if the agent runs on a limited computational budget and has a bounded memory of past data.
DeeSIL: Deep-Shallow Incremental Learning
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget.
On Deep Representation Learning from Noisy Web Images
The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classification challenge and related tasks.
Scalable domain adaptation of convolutional neural networks
Convolutional neural networks (CNNs) tend to become a standard approach to solve a wide array of computer vision problems.
