FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning

• URL: http://arxiv.org/abs/2111.11366v1
• Date: Mon, 22 Nov 2021 17:23:34 GMT
• Title: FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning
• Authors: Hichem Sahbi and Haoming Zhan
• Abstract summary: 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.
• Score: 14.924672048447338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) have recently achieved a great success in computer vision and several related fields. Despite such progress, current neural architectures still suffer from catastrophic interference (a.k.a. forgetting) which obstructs DNNs to learn continually. While several state-of-the-art methods have been proposed to mitigate forgetting, these existing solutions are either highly rigid (as regularization) or time/memory demanding (as replay). An intermediate class of methods, based on dynamic networks, has been proposed in the literature and provides a reasonable balance between task memorization and computational footprint. In this paper, 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, while training classifier parameters equates to Fisher discriminant analysis. The latter provides an effective incremental process which is also optimal from a Bayesian perspective. The trained features and classifiers are further enhanced using an incremental "end-to-end" fine-tuning. Extensive experiments, conducted on different challenging classification problems, show the high effectiveness of the proposed method.

Related papers

• A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers [8.343594411714934]
  Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. We propose a lifted training formulation based on Bregman distances for unfolded PNNs. We assess the behaviour of the proposed training approach for PNNs through numerical simulations on image denoising.
  arXiv  Detail & Related papers  (2024-08-16T13:41:34Z)
• Gradient-Free Training of Recurrent Neural Networks using Random Perturbations [1.1742364055094265]
  Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation algorithm by unrolling the RNN over time. BPTT suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. We present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT.
  arXiv  Detail & Related papers  (2024-05-14T21:15:29Z)
• 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)
• Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning [54.7584721943286]
  Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. We propose to only activate and select sparse neurons for learning current and past tasks at any stage.
  arXiv  Detail & Related papers  (2022-02-21T13:25:03Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Analytically Tractable Inference in Deep Neural Networks [0.0]
  Tractable Approximate Inference (TAGI) algorithm was shown to be a viable and scalable alternative to backpropagation for shallow fully-connected neural networks. We are demonstrating how TAGI matches or exceeds the performance of backpropagation, for training classic deep neural network architectures.
  arXiv  Detail & Related papers  (2021-03-09T14:51:34Z)
• Online Limited Memory Neural-Linear Bandits with Likelihood Matching [53.18698496031658]
  We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. We propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
  arXiv  Detail & Related papers  (2021-02-07T14:19:07Z)
• Designing Interpretable Approximations to Deep Reinforcement Learning [14.007731268271902]
  Deep neural networks (DNNs) set the bar for algorithm performance. It may not be feasible to actually use such high-performing DNNs in practice. This work seeks to identify reduced models that not only preserve a desired performance level, but also, for example, succinctly explain the latent knowledge represented by a DNN.
  arXiv  Detail & Related papers  (2020-10-28T06:33:09Z)
• A Novel Neural Network Training Framework with Data Assimilation [2.948167339160823]
  A gradient-free training framework based on data assimilation is proposed to avoid the calculation of gradients. The results show that the proposed training framework performed better than the gradient decent method.
  arXiv  Detail & Related papers  (2020-10-06T11:12:23Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• 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.