Initial Classifier Weights Replay for Memoryless Class Incremental Learning

Incremental Learning (IL) is useful when artificial systems need to deal with streams of data and do not have access to all data at all times. The most challenging setting requires a constant complexity of the deep model and an incremental model update without access to a bounded memory of past data. Then, the representations of past classes are strongly affected by catastrophic forgetting. To mitigate its negative effect, an adapted fine tuning which includes knowledge distillation is usually deployed. We propose a different approach based on a vanilla fine tuning backbone. It leverages initial classifier weights which provide a strong representation of past classes because they are trained with all class data. However, the magnitude of classifiers learned in different states varies and normalization is needed for a fair handling of all classes. Normalization is performed by standardizing the initial classifier weights, which are assumed to be normally distributed. In addition, a calibration of prediction scores is done by using state level statistics to further improve classification fairness. We conduct a thorough evaluation with four public datasets in a memoryless incremental learning setting. Results show that our method outperforms existing techniques by a large margin for large-scale datasets.Comment: Accepted in BMVC202

MultIOD: Rehearsal-free Multihead Incremental Object Detector

Class-Incremental learning (CIL) is the ability of artificial agents to accommodate new classes as they appear in a stream. It is particularly interesting in evolving environments where agents have limited access to memory and computational resources. The main challenge of class-incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one. Unfortunately, most existing class-incremental object detectors are applied to two-stage algorithms such as Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe that the current benchmarks are not realistic, and more effort should be dedicated to anchor-free and rehearsal-free object detection. In this context, we propose MultIOD, a class-incremental object detector based on CenterNet. Our main contributions are: (1) we propose a multihead feature pyramid and multihead detection architecture to efficiently separate class representations, (2) we employ transfer learning between classes learned initially and those learned incrementally to tackle catastrophic forgetting, and (3) we use a class-wise non-max-suppression as a post-processing technique to remove redundant boxes. Without bells and whistles, our method outperforms a range of state-of-the-art methods on two Pascal VOC datasets.Comment: Under review at the WACV 2024 conferenc

PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning

Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, finding a compromise between these two properties is particularly challenging when no memory buffer is available. Mainstream methods need to store two deep models since they integrate new classes using fine-tuning with knowledge distillation from the previous incremental state. We propose a method which has similar number of parameters but distributes them differently in order to find a better balance between plasticity and stability. Following an approach already deployed by transfer-based incremental methods, we freeze the feature extractor after the initial state. Classes in the oldest incremental states are trained with this frozen extractor to ensure stability. Recent classes are predicted using partially fine-tuned models in order to introduce plasticity. Our proposed plasticity layer can be incorporated to any transfer-based method designed for exemplar-free incremental learning, and we apply it to two such methods. Evaluation is done with three large-scale datasets. Results show that performance gains are obtained in all tested configurations compared to existing methods

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. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms

Apprentissage incrémental profond à large échelle

L'apprentissage incrémental (IL) permet l'adaptation d'agents artificiels à des environnements dynamiques dans lesquels les données sont présentées séquentiellement. Ce type d’apprentissage est nécessaire lorsque l'accès aux données passées est limité ou impossible, mais il est affecté par l'oubli catastrophique. Ce phénomène consiste en une chute drastique des performances des informations précédemment apprises lors de l'ingestion de nouvelles données. Une façon de résoudre ce problème est d'utiliser une mémoire limitée du passé pour rafraîchir les connaissances apprises précédemment. Actuellement, les approches basées sur la mémoire obtiennent les meilleurs résultats de l'état de l'art. Dans cette thèse, nous présentons plusieurs méthodes avec et sans mémoire du passé. Nos méthodes traitent l'oubli catastrophique soit (1) en calibrant les scores des classes passées et nouvelles à la fin du réseau, soit (2) en réutilisant les poids initiaux des classes passées, soit (3) en transférant les connaissances entre les datasets de référence et cibles. Nous étudions notamment l'utilité de la distillation largement utilisée et l'effet d'utiliser ou non une mémoire du passé. Des expériences approfondies contre des méthodes de l'état de l'art ont été menées afin de valider l'efficacité de nos méthodes.Incremental learning (IL) enables the adaptation of artificial agents to dynamic environments in which data is presented in streams. This type of learning is needed when access to past data is limited or impossible but is affected by catastrophic forgetting. This phenomenon consists of a drastic performance drop for previously learned information when ingesting new data. One way to tackle this problem is to use a limited memory of the past to refresh previously learned knowledge. Currently, memory-based approaches achieve the best state-of-the-art results. In this thesis, we present many methods with and without memory of the past. Our methods deal with catastrophic forgetting either by (1) calibrating past and new classes scores at the end of the network, or (2) performing initial class weights replay, or (3) transferring knowledge between reference and target datasets. We notably investigate the usefulness of the widely used knowledge distillation and the effect of enabling or not a memory of the past. Extensive experiments against a range of state-of-the-art approaches were conducted in order to validate the efficiency of our methods

Apprentissage incrémental profond à large échelle

Incremental learning (IL) enables the adaptation of artificial agents to dynamic environments in which data is presented in streams. This type of learning is needed when access to past data is limited or impossible but is affected by catastrophic forgetting. This phenomenon consists of a drastic performance drop for previously learned information when ingesting new data. One way to tackle this problem is to use a limited memory of the past to refresh previously learned knowledge. Currently, memory-based approaches achieve the best state-of-the-art results. In this thesis, we present many methods with and without memory of the past. Our methods deal with catastrophic forgetting either by (1) calibrating past and new classes scores at the end of the network, or (2) performing initial class weights replay, or (3) transferring knowledge between reference and target datasets. We notably investigate the usefulness of the widely used knowledge distillation and the effect of enabling or not a memory of the past. Extensive experiments against a range of state-of-the-art approaches were conducted in order to validate the efficiency of our methods.L'apprentissage incrémental (IL) permet l'adaptation d'agents artificiels à des environnements dynamiques dans lesquels les données sont présentées séquentiellement. Ce type d’apprentissage est nécessaire lorsque l'accès aux données passées est limité ou impossible, mais il est affecté par l'oubli catastrophique. Ce phénomène consiste en une chute drastique des performances des informations précédemment apprises lors de l'ingestion de nouvelles données. Une façon de résoudre ce problème est d'utiliser une mémoire limitée du passé pour rafraîchir les connaissances apprises précédemment. Actuellement, les approches basées sur la mémoire obtiennent les meilleurs résultats de l'état de l'art. Dans cette thèse, nous présentons plusieurs méthodes avec et sans mémoire du passé. Nos méthodes traitent l'oubli catastrophique soit (1) en calibrant les scores des classes passées et nouvelles à la fin du réseau, soit (2) en réutilisant les poids initiaux des classes passées, soit (3) en transférant les connaissances entre les datasets de référence et cibles. Nous étudions notamment l'utilité de la distillation largement utilisée et l'effet d'utiliser ou non une mémoire du passé. Des expériences approfondies contre des méthodes de l'état de l'art ont été menées afin de valider l'efficacité de nos méthodes

IL2M: Class incremental learning with dual memory

International audienceThis paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition. First, we simplify the current fine tuning based approaches which use a combination of classification and distillation losses to compensate for the limited availability of past data. We find that the distillation term actually hurts performance when a memory is allowed. Then, we modify the usual class IL memory component. Similar to existing works, a first memory stores exemplar images of past classes. A second memory is introduced here to store past class statistics obtained when they were initially learned. The intuition here is that classes are best modeled when all their data are available and that their initial statistics are useful across different incremental states. A prediction bias towards newly learned classes appears during inference because the dataset is imbalanced in their favor. The challenge is to make predictions of new and past classes more comparable. To do this, scores of past classes are rectified by leveraging contents from both memories. The method has negligible added cost, both in terms of memory and of inference complexity. Experiments with three large public datasets show that the proposed approach is more effective than a range of competitive state-of-the-art methods

ScaIL: Classifier Weights Scaling for Class Incremental Learning

International audienceIncremental learning is useful if an AI agent needs to integrate data from a stream. The problem is non trivial if the agent runs on a limited computational budget and has a bounded memory of past data. In a deep learning approach, the constant computational budget requires the use of a fixed architecture for all incremental states. The bounded memory generates imbalance in favor of new classes and a prediction bias toward them appears. This bias is commonly countered by introducing a data balancing step in addition to the basic network training. We depart from this approach and propose simple but efficient scaling of past classifiers' weights to make them more comparable to those of new classes. Scaling exploits incremental state statistics and is applied to the classifiers learned in the initial state of classes to profit from all their available data. We also question the utility of the widely used distillation loss component of incremental learning algorithms by comparing it to vanilla fine tuning in presence of a bounded memory. Evaluation is done against competitive baselines using four public datasets. Results show that the classifier weights scaling and the removal of the distillation are both beneficial

A comprehensive study of class incremental learning algorithms for visual tasks

International audienceThe ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i.e., the tendency of neural networks to underfit past data when new ones are ingested. A first group of approaches tackles forgetting by increasing deep model capacity to accommodate new knowledge. 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. While the first type of algorithms were compared thoroughly, this is not the case for methods which exploit a fixed size model. Here, we focus on the latter, place them in a common conceptual and experimental framework and propose the following contributions: (1) define six desirable properties of incremental learning algorithms and analyze them according to these properties, (2) introduce a unified formalization of the class-incremental learning problem, (3) propose a common evaluation framework which is more thorough than existing ones in terms of number of datasets, size of datasets, size of bounded memory and number of incremental states, (4) investigate the usefulness of herding for past exemplars selection, (5) provide experimental evidence that it is possible to obtain competitive performance without the use of knowledge distillation to tackle catastrophic forgetting and (6) facilitate reproducibility by integrating all tested methods in a common open-source repository. The main experimental finding is that none of the existing algorithms achieves the best results in all evaluated settings. Important differences arise notably if a bounded memory of past classes is allowed or not

A comparative study of calibration methods for imbalanced class incremental learning

International audienceDeep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the capacity to handle streams of visual information and the management of class imbalance in datasets. Existing research approaches these two problems separately while they co-occur in real world applications. Here, we study the problem of learning incrementally from imbalanced datasets. We focus on algorithms which have a constant deep model complexity and use a bounded memory to store exemplars of old classes across incremental states. 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. A score prediction bias in favor of new classes appears and we evaluate a comprehensive set of score calibration methods to reduce it. Evaluation is carried with three datasets, using two dataset imbalance configurations and three bounded memory sizes. Results show that most calibration methods have beneficial effect and that they are most useful for lower bounded memory sizes, which are most interesting in practice. As a secondary contribution, we remove the usual distillation component from the loss function of incremental learning algorithms. We show that simpler vanilla fine tuning is a stronger backbone for imbalanced incremental learning algorithms