ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep Networks
- URL: http://arxiv.org/abs/2411.10891v2
- Date: Sat, 23 Nov 2024 21:49:24 GMT
- Title: ChannelDropBack: Forward-Consistent Stochastic Regularization for Deep Networks
- Authors: Evgeny Hershkovitch Neiterman, Gil Ben-Artzi,
- Abstract summary: Existing techniques often require modifying the architecture of the network by adding specialized layers.
We present ChannelDropBack, a simple regularization approach that introduces randomness only into the backward information flow.
It allows for seamless integration into the training process of any model and layers without the need to change its architecture.
- Score: 5.00301731167245
- License:
- Abstract: Incorporating stochasticity into the training process of deep convolutional networks is a widely used technique to reduce overfitting and improve regularization. Existing techniques often require modifying the architecture of the network by adding specialized layers, are effective only to specific network topologies or types of layers - linear or convolutional, and result in a trained model that is different from the deployed one. We present ChannelDropBack, a simple stochastic regularization approach that introduces randomness only into the backward information flow, leaving the forward pass intact. ChannelDropBack randomly selects a subset of channels within the network during the backpropagation step and applies weight updates only to them. As a consequence, it allows for seamless integration into the training process of any model and layers without the need to change its architecture, making it applicable to various network topologies, and the exact same network is deployed during training and inference. Experimental evaluations validate the effectiveness of our approach, demonstrating improved accuracy on popular datasets and models, including ImageNet and ViT. Code is available at \url{https://github.com/neiterman21/ChannelDropBack.git}.
Related papers
- Adaptive Depth Networks with Skippable Sub-Paths [1.8416014644193066]
We present a practical approach to adaptive depth networks with minimal training effort.
Our approach does not train every target sub-network in an iterative manner.
We provide a formal rationale for why the proposed training method can reduce overall prediction errors.
arXiv Detail & Related papers (2023-12-27T03:43:38Z) - Improving the Trainability of Deep Neural Networks through Layerwise
Batch-Entropy Regularization [1.3999481573773072]
We introduce and evaluate the batch-entropy which quantifies the flow of information through each layer of a neural network.
We show that we can train a "vanilla" fully connected network and convolutional neural network with 500 layers by simply adding the batch-entropy regularization term to the loss function.
arXiv Detail & Related papers (2022-08-01T20:31:58Z) - Manifold Regularized Dynamic Network Pruning [102.24146031250034]
This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks.
The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
arXiv Detail & Related papers (2021-03-10T03:59:03Z) - All at Once Network Quantization via Collaborative Knowledge Transfer [56.95849086170461]
We develop a novel collaborative knowledge transfer approach for efficiently training the all-at-once quantization network.
Specifically, we propose an adaptive selection strategy to choose a high-precision enquoteteacher for transferring knowledge to the low-precision student.
To effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
arXiv Detail & Related papers (2021-03-02T03:09:03Z) - Local Critic Training for Model-Parallel Learning of Deep Neural
Networks [94.69202357137452]
We propose a novel model-parallel learning method, called local critic training.
We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
We also show that trained networks by the proposed method can be used for structural optimization.
arXiv Detail & Related papers (2021-02-03T09:30:45Z) - Neural Subdivision [58.97214948753937]
This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling.
We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category.
We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
arXiv Detail & Related papers (2020-05-04T20:03:21Z) - Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs.
Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-06T15:51:00Z) - Side-Tuning: A Baseline for Network Adaptation via Additive Side
Networks [95.51368472949308]
Adaptation can be useful in cases when training data is scarce, or when one wishes to encode priors in the network.
In this paper, we propose a straightforward alternative: side-tuning.
arXiv Detail & Related papers (2019-12-31T18:52:32Z)
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.