Paper proposes CNE-net to tackle incremental learning in (T)ACSA tasks.
problem Catastrophic forgetting in multi-task incremental learning for (T)ACSA.
method Category Name Embedding network (CNE-net) with shared encoder and decoder.
result State-of-the-art performance on (T)ACSA benchmark datasets.
It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge. However, these strategies are still unsatisfactory if the tasks are not disjoint but constitute a single increme…
DPTA improves CIL by adapting PTMs with dual prototypes.
problem Catastrophic forgetting in incremental learning with pre-trained models.
method Dual-Prototype Network with Task-wise Adaptation (DPTA).
result DPTA consistently outperforms recent methods by 1\%-5\% on multiple benchmarks.
Meta-learning approach prevents forgetting across tasks.
problem Catastrophic forgetting in deep neural networks.
method Incremental Task-Agnostic Meta-learning (iTAML) with a meta-update rule.
result Significant improvements in class-incremental learning tasks.
One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any attempts at learning new tasks incrementally cause them to completely forget about pr…
EVCL combines VCL and EWC to prevent forgetting new tasks.
problem Preventing catastrophic forgetting in continual learning.
method Hybrid model integrating VCL and EWC.
result Consistently outperforms baselines in learning new tasks.
A new model learns preferences incrementally without personal data.
problem Incremental session-based recommendation without personal data.
method Memory Augmented Neural model (MAN) that combines a neural recommender with a nonparametric memory.
result MAN consistently outperforms existing methods in incremental session-based recommendation.
BI-MAML learns multiple tasks without forgetting old ones.
problem Catastrophic forgetting in meta learning.
method Incremental model adaptation with balanced learning strategy.
result BI-MAML outperforms state-of-the-art models in accuracy and efficiency.
Meta-learning approach improves object detection on new classes.
problem Deterioration of object detection performance on old classes in incremental settings.
method Meta-learning to reshape model gradients for optimal task adaptation.
result Meta-learning approach outperforms state-of-the-art methods in incremental object detection.
TKIL improves class-balanced performance in incremental learning.
problem Catastrophic forgetting in sequential learning tasks.
method Introduces Tangent Kernel for Incremental Learning (TKIL) based on Neural Tangent Kernel (NTK).
result TKIL achieves better overall accuracy and variance across classes.
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinde…
DoubleAdapt improves stock trend forecasting by adapting models to evolving data.
problem Incremental learning for stock trend forecasting is challenging due to distribution shifts.
method DoubleAdapt framework with two adapters for data and model adaptation.
result DoubleAdapt achieves state-of-the-art predictive performance on real-world stock datasets.
Paper presents IMRCs for evolving tasks with forward and backward learning.
problem Incremental learning of evolving tasks with few samples per task.
method Incremental minimax risk classifiers (IMRCs) that exploit forward and backward learning.
result IMRCs provide significant performance improvement, especially with reduced sample sizes.
Improved incremental sequence classification with temporal consistency.
problem Updating predictions as new sequence elements are revealed.
method Temporal-difference learning and a temporal-consistency condition for successive predictions.
result Optimizing a novel loss function improves data efficiency and predictive accuracy.
FedGTEA learns new tasks in federated learning with task embeddings and alignment.
problem Federated class-incremental learning with task-specific knowledge and model uncertainty.
method Cardinality-Agnostic Task Encoder (CATE) for Gaussian task embeddings, 2-Wasserstein distance for inter-task alignment.
result FedGTEA achieves superior classification performance and mitigates forgetting.
Paper proposes a method to maintain ASR performance on new tasks without forgetting old ones.
problem Mitigating forgetting in ASR models when learning new tasks.
method A novel explainability-based knowledge distillation combined with response-based knowledge distillation.
result Our method outperforms existing ones in mitigating forgetting on multi-stage sequential training tasks.
New method for incremental meta-learning reduces forgetting and improves performance.
problem Incremental meta-learning for few-shot classification tasks.
method Indirect Discriminant Alignment (IDA) with incremental updates.
result Incremental learning reduces forgetting and improves performance.
Paper proposes a method to learn multiple tasks without forgetting, maintaining model compactness.
problem Lifelong learning in deep learning models, especially forgetting of previous tasks.
method Combines deep model compression, critical weights selection, and progressive network expansion in an iterative manner.
result Incremental learning without forgetting, maintaining model compactness.
Proposes efficient model for continual learning that grows model over task-specific parameters.
problem Limited transfer learning ability and forgetting of earlier knowledge in existing methods.
method Filter and channel expansion method that grows model over previous task parameters.
result Better knowledge transfer and improved performance in task incremental learning.
Incremental learning suffers from two challenging problems; forgetting of old knowledge and intransigence on learning new knowledge. Prediction by the model incrementally learned with a subset of the dataset are thus uncertain and the uncertainty accumulates through the tasks by knowledge transfer. To prevent overfitti…
Paper tackles graph class-incremental learning with task profiling and prompting.
problem Challenges in separating classes from different tasks in graph CIL.
method Laplacian smoothing-based task profiling and graph prompting approach.
result 100% task ID prediction accuracy and significant performance improvement.
CNAS optimizes neural architectures for class-incremental learning.
problem Capacity saturation in static neural architectures for class-incremental learning.
method CNAS uses reinforcement learning and network transformations to adaptively select architectures.
result CNAS outperforms static architectures and is more efficient.
AdaAnn optimizes annealing for efficient probability density approximation.
problem Efficiently approximating complex probability distributions with multiple modes.
method AdaAnn is an adaptive annealing scheduler that adjusts temperature increments based on KL divergence.
result AdaAnn improves computational efficiency in variational inference and parameter estimation.
The main purpose of incremental learning is to learn new knowledge while not forgetting the knowledge which have been learned before. At present, the main challenge in this area is the catastrophe forgetting, namely the network will lose their performance in the old tasks after training for new tasks. In this paper, we…
Transformers learn to integrate information from past positions incrementally, specializing heads in distinct patterns.
problem How transformers learn to integrate information from multiple past positions with varying statistical significance.
method High-order Markov chain task, incremental learning, sparse attention patterns, simplified differential equations, stage-wise convergence, early stopping as regularizer.
result Transformers learn to specialize heads in distinct patterns, shifting from competitive to cooperative learning dynamics.
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Se…
Comprehensive surgical planning require complex patient-specific anatomical models. For instance, functional muskuloskeletal simulations necessitate all relevant structures to be segmented, which could be performed in real-time using deep neural networks given sufficient annotated samples. Such large datasets of multip…
Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffec…
Paper tackles catastrophic forgetting in neural networks with an adversarial feature alignment method.
problem Dramatic performance degradation when new tasks are added to an existing neural network model.
method Inspired by human learning, the paper proposes an adversarial feature alignment method to decompose complex tasks into easier goals.
result The proposed method outperforms state-of-the-art methods in both accuracies on new tasks and performance preservation on old tasks.
DeGAN enriches data from related domains for future learning tasks.
problem Lack of relevant data for future learning tasks like Model Compression and Incremental Learning.
method Data-Enriching GAN (DeGAN) framework to retrieve representative samples from a trained classifier.
result State-of-the-art performance for Data-free Knowledge Distillation and Incremental Learning on benchmark datasets.
This paper tackles federated incremental learning with dynamic memory allocation for improved model performance in non-IID data.
problem Catastrophic forgetting in federated healthcare systems with non-IID data.
method Dynamic memory allocation strategy based on data replay mechanism.
result Significant performance improvements in medical image datasets compared to baseline models.
We introduce a novel sensitivity analysis framework for large scale classification problems that can be used when a small number of instances are incrementally added or removed. For quickly updating the classifier in such a situation, incremental learning algorithms have been intensively studied in the literature. Alth…
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that map features to a high dimensional non-linear space. However, most…
Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning. The general recipe for computing CV estimate is to run a learning algorithm separately for each CV fold, a computationally expensive process. In this paper, we propose a new approach to reduce the computa…
Paper tackles class-incremental learning by combining self-supervised learning to mitigate prior information loss.
problem Catastrophic forgetting and prior information loss in class-incremental learning.
method Combining self-supervised learning with class-incremental learning to mitigate prior information loss.
result Proposed method outperforms state-of-the-art methods.
SpaceNet improves continual learning by intelligently compressing neural connections.
problem Catastrophic forgetting in sequential learning tasks.
method SpaceNet trains sparse deep neural networks adaptively, compressing task-specific connections.
result SpaceNet outperforms existing methods in class incremental learning scenarios.
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics appl…
PEAKS selects key training examples incrementally based on prediction error and kernel similarity.
problem Dynamic data selection in deep learning models.
method Prediction Error Anchored by Kernel Similarity (PEAKS) for incremental data selection.
result PEAKS outperforms existing selection strategies and yields better performance returns as training data size grows.
Algorithm estimates bounds of updated classifier coefficients efficiently.
problem Determining sensitivity of updated classifiers without retraining.
method Proposes an algorithm to estimate upper and lower bounds of updated classifier coefficients.
result Estimates bounds with low computational complexity and tightness.
This thesis tackles continual learning in neural networks, overcoming forgetting.
problem Overcoming catastrophic forgetting in neural networks.
method Task incremental and online continual learning settings; proposed methods for mitigating forgetting.
result Advances in continual learning state of the art and challenges for application.
Class incremental learning refers to a special multi-class classification task, in which the number of classes is not fixed but is increasing with the continual arrival of new data. Existing researches mainly focused on solving catastrophic forgetting problem in class incremental learning. To this end, however, these m…
In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve i…
Resultant improves likelihood-based U-OOD detection across various tasks.
problem Improving likelihood-based U-OOD detection performance.
method Resultant combines post-hoc prior and dataset entropy-mutual calibration techniques.
result Resultant achieves new state-of-the-art U-OOD detection performance.
Incremental class learning, a scenario in continual learning context where classes and their training data are sequentially and disjointedly observed, challenges a problem widely known as catastrophic forgetting. In this work, we propose a novel incremental class learning method that can significantly reduce memory ove…
We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning …
Paper proposes a new method to select memory data for online class-incremental learning.
problem Selecting which buffered images to replay for online class-incremental learning.
method Adversarial Shapley value scoring method to preserve latent decision boundaries.
result Proposed ASER method provides competitive or improved performance compared to state-of-the-art methods.
Sparse Meta Networks adapt deep neural networks incrementally for fast learning.
problem Training deep neural networks is slow and impractical for complex, changing environments.
method Sparse Meta Networks use a memory layer to learn online sequential adaptation, accumulating fast-weights incrementally.
result Sparse Meta Networks achieve strong performance in various sequential adaptation scenarios.
Wield simplifies reinforcement learning task design.
problem Lack of suitable task representations for reinforcement learning.
method Introduces Wield, a system with software primitives and staged randomization protocol.
result Facilitates practical reinforcement learning through decoupling system and task design.