Self-Contrastive Forward-Forward Algorithm

• URL: http://arxiv.org/abs/2409.11593v2
• Date: Thu, 27 Mar 2025 15:57:57 GMT
• Title: Self-Contrastive Forward-Forward Algorithm
• Authors: Xing Chen, Dongshu Liu, Jeremie Laydevant, Julie Grollier,
• Abstract summary: Forward-Forward (FF) algorithm relies on feedforward operations to optimize layer-wise objectives.<n>FF has failed to reach state-of-the-art performance on most standard benchmark tasks.<n>We propose Self-Contrastive Forward-Forward (SCFF) algorithm, a competitive training method aimed at closing this performance gap.
• Score: 3.1361717406527667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agents that operate autonomously benefit from lifelong learning capabilities. However, compatible training algorithms must comply with the decentralized nature of these systems, which imposes constraints on both the parameter counts and the computational resources. The Forward-Forward (FF) algorithm is one of these. FF relies only on feedforward operations, the same used for inference, for optimizing layer-wise objectives. This purely forward approach eliminates the need for transpose operations required in traditional backpropagation. Despite its potential, FF has failed to reach state-of-the-art performance on most standard benchmark tasks, in part due to unreliable negative data generation methods for unsupervised learning. In this work, we propose the Self-Contrastive Forward-Forward (SCFF) algorithm, a competitive training method aimed at closing this performance gap. Inspired by standard self-supervised contrastive learning for vision tasks, SCFF generates positive and negative inputs applicable across various datasets. The method demonstrates superior performance compared to existing unsupervised local learning algorithms on several benchmark datasets, including MNIST, CIFAR-10, STL-10, and Tiny ImageNet. We extend FF's application to training recurrent neural networks, expanding its utility to sequential data tasks. These findings pave the way for high-accuracy, real-time learning on resource-constrained edge devices.

Related papers

• Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning [20.037634881772842]
  Forward-Forward (FF) algorithm was recently proposed as a local learning method to address the limitations of backpropagation (BP) We reformulate FF using distance metric learning and propose a distance-forward algorithm (DF) to improve FF performance in supervised vision tasks. Our method surpasses existing FF models and other advanced local learning approaches, with accuracies of 99.7% on MNIST, 88.2% on CIFAR-10, 59% on CIFAR-100, 95.9% on SVHN, and 82.5% on ImageNette.
  arXiv  Detail & Related papers  (2024-08-27T10:01:43Z)
• Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm [5.1246638322893245]
  Forward-Forward (FF) algorithm has been proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. We take the main ideas of FF and improve them by leveraging channel-wise competitive learning in the context of convolutional neural networks for image classification tasks. Our method outperforms recent FF-based models on image classification tasks, achieving testing errors of 0.58%, 7.69%, 21.89%, and 48.77% on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 respectively.
  arXiv  Detail & Related papers  (2023-12-19T23:48:43Z)
• An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning [0.8158530638728501]
  Existing learning methods often struggle to balance interpretability and predictive performance. Our approach aims to strike a better balance between these two aspects through the use of a building block based on NMF.
  arXiv  Detail & Related papers  (2023-11-20T02:00:33Z)
• A Study of Forward-Forward Algorithm for Self-Supervised Learning [65.268245109828]
  We study the performance of forward-forward vs. backpropagation for self-supervised representation learning. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-supervised) training, the transfer performance is significantly lagging behind in all the studied settings.
  arXiv  Detail & Related papers  (2023-09-21T10:14:53Z)
• Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics [9.741019160068388]
  We introduce the Cost-Sensitive Self-Training (CSST) framework which generalizes the self-training-based methods for optimizing non-decomposable metrics. Our results demonstrate that CSST achieves an improvement over the state-of-the-art in majority of the cases across datasets and objectives.
  arXiv  Detail & Related papers  (2023-04-28T10:31:12Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• Efficient Few-Shot Object Detection via Knowledge Inheritance [62.36414544915032]
  Few-shot object detection (FSOD) aims at learning a generic detector that can adapt to unseen tasks with scarce training samples. We present an efficient pretrain-transfer framework (PTF) baseline with no computational increment. We also propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights.
  arXiv  Detail & Related papers  (2022-03-23T06:24:31Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning [14.924672048447338]
  We devise a dynamic network architecture for continual learning based on a novel forgetting-free neural block (FFNB) Training FFNB features on new tasks is achieved using a novel procedure that constrains the underlying parameters in the null-space of the previous tasks.
  arXiv  Detail & Related papers  (2021-11-22T17:23:34Z)
• SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption [72.35532598131176]
  We propose SCARF, a technique for contrastive learning, where views are formed by corrupting a random subset of features. We show that SCARF complements existing strategies and outperforms alternatives like autoencoders.
  arXiv  Detail & Related papers  (2021-06-29T08:08:33Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• Optimal Task Assignment to Heterogeneous Federated Learning Devices [0.0]
  We investigate the problem of minimizing the duration of Federated Learning rounds by controlling how much data each device uses for training. We propose a make-time algorithm named OLAR and prove that it provides optimal schedules. Our results indicate that OLAR provides optimal solutions with a small execution time.
  arXiv  Detail & Related papers  (2020-10-01T07:58:48Z)
• Fully Convolutional Networks for Continuous Sign Language Recognition [83.85895472824221]
  Continuous sign language recognition is a challenging task that requires learning on both spatial and temporal dimensions. We propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences.
  arXiv  Detail & Related papers  (2020-07-24T08:16:37Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.