Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
- URL: http://arxiv.org/abs/2405.17779v1
- Date: Tue, 28 May 2024 03:19:15 GMT
- Title: Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
- Authors: Huiping Zhuang, Di Fang, Kai Tong, Yuchen Liu, Ziqian Zeng, Xu Zhou, Cen Chen,
- Abstract summary: We propose an Analytic Exemplar-Free Online Continual Learning (AEF-OCL)
The AEF-OCL leverages analytic continual learning principles and employs ridge regression as a classifier for features extracted by a large backbone network.
Experimental results demonstrate that despite being an exemplar-free strategy, our method outperforms various methods on the autonomous driving SODA10M dataset.
- Score: 25.38082751323396
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the field of autonomous driving, even a meticulously trained model can encounter failures when faced with unfamiliar sceanrios. One of these scenarios can be formulated as an online continual learning (OCL) problem. That is, data come in an online fashion, and models are updated according to these streaming data. Two major OCL challenges are catastrophic forgetting and data imbalance. To address these challenges, in this paper, we propose an Analytic Exemplar-Free Online Continual Learning (AEF-OCL). The AEF-OCL leverages analytic continual learning principles and employs ridge regression as a classifier for features extracted by a large backbone network. It solves the OCL problem by recursively calculating the analytical solution, ensuring an equalization between the continual learning and its joint-learning counterpart, and works without the need to save any used samples (i.e., exemplar-free). Additionally, we introduce a Pseudo-Features Generator (PFG) module that recursively estimates the deviation of real features. The PFG generates offset pseudo-features following a normal distribution, thereby addressing the data imbalance issue. Experimental results demonstrate that despite being an exemplar-free strategy, our method outperforms various methods on the autonomous driving SODA10M dataset. Source code is available at https://github.com/ZHUANGHP/Analytic-continual-learning.
Related papers
- Online Cascade Learning for Efficient Inference over Streams [9.516197133796437]
Large Language Models (LLMs) have a natural role in answering complex queries about data streams.
We propose online cascade learning, the first approach to address this challenge.
We formulate the task of learning cascades online as an imitation-learning problem.
arXiv Detail & Related papers (2024-02-07T01:46:50Z) - Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps.
We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights.
In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
arXiv Detail & Related papers (2023-06-14T11:41:42Z) - Causal Deep Reinforcement Learning Using Observational Data [11.790171301328158]
We propose two deconfounding methods in deep reinforcement learning (DRL)
The methods first calculate the importance degree of different samples based on the causal inference technique, and then adjust the impact of different samples on the loss function.
We prove the effectiveness of our deconfounding methods and validate them experimentally.
arXiv Detail & Related papers (2022-11-28T14:34:39Z) - Granger Causality using Neural Networks [8.835231777363399]
We present several new classes of models that can handle underlying non-linearity.
We show one can directly decouple lags and individual time series importance via decoupled penalties.
We also show one can directly decouple lags and individual time series importance via decoupled penalties.
arXiv Detail & Related papers (2022-08-07T12:02:48Z) - Federated Latent Class Regression for Hierarchical Data [5.110894308882439]
Federated Learning (FL) allows a number of agents to participate in training a global machine learning model without disclosing locally stored data.
We propose a novel probabilistic model, Hierarchical Latent Class Regression (HLCR), and its extension to Federated Learning, FEDHLCR.
Our inference algorithm, being derived from Bayesian theory, provides strong convergence guarantees and good robustness to overfitting. Experimental results show that FEDHLCR offers fast convergence even in non-IID datasets.
arXiv Detail & Related papers (2022-06-22T00:33:04Z) - Winning solutions and post-challenge analyses of the ChaLearn AutoDL
challenge 2019 [112.36155380260655]
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series.
Results show that DL methods dominated, though popular Neural Architecture Search (NAS) was impractical.
A high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator"
arXiv Detail & Related papers (2022-01-11T06:21:18Z) - Task-agnostic Continual Learning with Hybrid Probabilistic Models [75.01205414507243]
We propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification.
The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting.
We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST.
arXiv Detail & Related papers (2021-06-24T05:19:26Z) - Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition [55.362258027878966]
We present momentum pseudo-labeling (MPL) as a simple yet effective strategy for semi-supervised speech recognition.
MPL consists of a pair of online and offline models that interact and learn from each other, inspired by the mean teacher method.
The experimental results demonstrate that MPL effectively improves over the base model and is scalable to different semi-supervised scenarios.
arXiv Detail & Related papers (2021-06-16T16:24:55Z) - Self-Damaging Contrastive Learning [92.34124578823977]
Unlabeled data in reality is commonly imbalanced and shows a long-tail distribution.
This paper proposes a principled framework called Self-Damaging Contrastive Learning to automatically balance the representation learning without knowing the classes.
Our experiments show that SDCLR significantly improves not only overall accuracies but also balancedness.
arXiv Detail & Related papers (2021-06-06T00:04:49Z) - Learning summary features of time series for likelihood free inference [93.08098361687722]
We present a data-driven strategy for automatically learning summary features from time series data.
Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
arXiv Detail & Related papers (2020-12-04T19:21:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.