11 research outputs found
Prediction Error-based Classification for Class-Incremental Learning
Class-incremental learning (CIL) is a particularly challenging variant of
continual learning, where the goal is to learn to discriminate between all
classes presented in an incremental fashion. Existing approaches often suffer
from excessive forgetting and imbalance of the scores assigned to classes that
have not been seen together during training. In this study, we introduce a
novel approach, Prediction Error-based Classification (PEC), which differs from
traditional discriminative and generative classification paradigms. PEC
computes a class score by measuring the prediction error of a model trained to
replicate the outputs of a frozen random neural network on data from that
class. The method can be interpreted as approximating a classification rule
based on Gaussian Process posterior variance. PEC offers several practical
advantages, including sample efficiency, ease of tuning, and effectiveness even
when data are presented one class at a time. Our empirical results show that
PEC performs strongly in single-pass-through-data CIL, outperforming other
rehearsal-free baselines in all cases and rehearsal-based methods with moderate
replay buffer size in most cases across multiple benchmarks
Energy-Based Models for Continual Learning
We motivate Energy-Based Models (EBMs) as a promising model class for
continual learning problems. Instead of tackling continual learning via the use
of external memory, growing models, or regularization, EBMs have a natural way
to support a dynamically-growing number of tasks or classes that causes less
interference with previously learned information. Our proposed version of EBMs
for continual learning is simple, efficient and outperforms baseline methods by
a large margin on several benchmarks. Moreover, our proposed contrastive
divergence based training objective can be applied to other continual learning
methods, resulting in substantial boosts in their performance. We also show
that EBMs are adaptable to a more general continual learning setting where the
data distribution changes without the notion of explicitly delineated tasks.
These observations point towards EBMs as a class of models naturally inclined
towards the continual learning regime
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Brain-inspired replay for continual learning with artificial neural networks.
Funder: International Brain Research Organization (IBRO); doi: https://doi.org/10.13039/501100001675Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these networks are trained on something new, they rapidly forget what was learned before. In the brain, a mechanism thought to be important for protecting memories is the reactivation of neuronal activity patterns representing those memories. In artificial neural networks, such memory replay can be implemented as 'generative replay', which can successfully - and surprisingly efficiently - prevent catastrophic forgetting on toy examples even in a class-incremental learning scenario. However, scaling up generative replay to complicated problems with many tasks or complex inputs is challenging. We propose a new, brain-inspired variant of replay in which internal or hidden representations are replayed that are generated by the network's own, context-modulated feedback connections. Our method achieves state-of-the-art performance on challenging continual learning benchmarks (e.g., class-incremental learning on CIFAR-100) without storing data, and it provides a novel model for replay in the brain
Omnidirectional Transfer for Quasilinear Lifelong Learning
In biological learning, data are used to improve performance not only on the
current task, but also on previously encountered and as yet unencountered
tasks. In contrast, classical machine learning starts from a blank slate, or
tabula rasa, using data only for the single task at hand. While typical
transfer learning algorithms can improve performance on future tasks, their
performance on prior tasks degrades upon learning new tasks (called
catastrophic forgetting). Many recent approaches for continual or lifelong
learning have attempted to maintain performance given new tasks. But striving
to avoid forgetting sets the goal unnecessarily low: the goal of lifelong
learning, whether biological or artificial, should be to improve performance on
all tasks (including past and future) with any new data. We propose
omnidirectional transfer learning algorithms, which includes two special cases
of interest: decision forests and deep networks. Our key insight is the
development of the omni-voter layer, which ensembles representations learned
independently on all tasks to jointly decide how to proceed on any given new
data point, thereby improving performance on both past and future tasks. Our
algorithms demonstrate omnidirectional transfer in a variety of simulated and
real data scenarios, including tabular data, image data, spoken data, and
adversarial tasks. Moreover, they do so with quasilinear space and time
complexity
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
Three types of incremental learning.
Funder: International Brain Research Organization (IBRO); doi: https://doi.org/10.13039/501100001675Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed, but comparing their performances is difficult due to the lack of a common framework. To help address this, we describe three fundamental types, or 'scenarios', of continual learning: task-incremental, domain-incremental and class-incremental learning. Each of these scenarios has its own set of challenges. To illustrate this, we provide a comprehensive empirical comparison of currently used continual learning strategies, by performing the Split MNIST and Split CIFAR-100 protocols according to each scenario. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of the effectiveness of different strategies. The proposed categorization aims to structure the continual learning field, by forming a key foundation for clearly defining benchmark problems