How transferable are features in deep neural networks (2009) using random filters at the first layer can work almost as well as learned features in 2-layer networks. Deep learning in neural networks: An overview Neural Networks, 2015 Global geometric similarity scheme for feature selection in fault diagnosis Expert Systems with Applications, 2014 Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications Mechanical Systems and Signal Processing, 2014 Abstract—We address vehicle detection on rear view vehicle images captured from a distance along multi-lane highways, and vehicle classification using transferable features from Deep Neural Dec 9, 2014 · AI-powered analysis of 'How transferable are features in deep neural networks?'. Hence, it is important to formally reduce the Abstract Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Although capable of learning highly nonlinear features, deep neural networks are very prone to overfitting (Peng et al. However, most of the existing studies have been performed with the assumption that the same Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer Jun 22, 2022 · Numerous deep neural networks have been trained on natural images, and the thing is that they share curious phenomenon. Adapting a method proposed by Yosinski et al. Dec 8, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. We envision that the loss of transferability mainly stems from the Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Jan 1, 2021 · The computationally expensive nature of Deep Neural Networks, along with their significant hunger for labeled data, can impair the overall performance of these models. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. Fortunately, neural networks can be trained in While deep neural networks are more powerful for learning general and transferable features, the latest findings also re-veal that the deep features must eventually transition from general to specific along the network, and feature transfer-ability drops significantly in higher layers with increasing domain discrepancy. Dec. Aug 6, 2017 · Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In other words, the features computed in higher layers of the network must Nov 6, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. These features seem to be generic — useful for many datasets and tasks — as opposed to specific — useful for only one dataset and task. Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Oct 7, 2020 · In order to intuitively judge and validate the effectiveness of the proposed transfer neural network with hybrid method in domain adaptation, we implement experiments to visualize the distribution as well as the discriminative area of deep features learned by the trained transfer model from source and target domains. Features must eventually transition from general to specific by the Many deep neural networks trained on natural images exhibit a curious phe- nomenon in common: on the rst layer they learn features similar to Gabor lters and color blobs. Features must eventually transition from general to specific by the How transferable are features in deep neural networks? By: Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson Presentation by Anthony Chen antchen@ucdavis. Here we study convolutional neural network-based acoustic models in the context of automatic speech recog-nition. Features must eventually transition from general to specific This paper aims to quantify the transferrability of features in deep neural networks, both in terms of the difference between source and target tasks and in terms of the depth of the features being transferred. How transferable are features in deep neural networks? Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Sep 16, 2019 · This process will tend to work if the features are general, meaning suitable to both base and target tasks, instead of specific to the base task. Aug 6, 2025 · Transfer learning is a general technique that can be applied to any type of model, such as traditional machine learning models like decision trees, random forests, and support vector machines or deep neural networks. How-ever, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Intuitively, if AnB performs as well as baseB, there is evidence that until the n-block the features are general in respect of task B. Problem: Neural networks seem to work in two parts, the first part extracts general features and the second solve the target task. Abstract Characterization of the representations learned in intermediate layers of deep net-works can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies. Mar 14, 2023 · Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task. Such rst-layer features appear not to be specic to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific Mar 6, 2024 · Abstract Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. This review will play an emphasis on convolutional neural network (CNN). edu Code for paper "How transferable are features in deep neural networks?" - yosinski/convnet_transfer Nov 6, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Poster How transferable are features in deep neural networks? Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson [ Abstract ] [ PDF] 2014 Poster Jul 1, 2025 · To overcome this, we propose Residual Quanvolutional Neural Networks (ResQuNNs), which utilize residual learning by adding skip connections between quanvolutional layers. Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Features must eventually transition from general to specific 一. Typically, initial layers of a CNN learn features that resemble Gabor filter or color blobs, and are fairly general, while the later layers are more task-specific. 9 December 2014 Jason Yosinski How transferable are features in deep neural networks? Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. Features must eventually transition from general to specific by the Experiment 3:Random Weights In comparison, Jarrett et al. Features must eventually transition from general to specific by the This network is a control for the transfer network (AnB). Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks Recent years have witnessed increasing popularity and development of deep learning spanning through various fields. Features must eventually transition from general to specific Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Features must eventually transition from general to specic by the last layer Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. 前言 《How transferable are features in deep neural networks?》发表在 2014 年的机器学习顶级会议 NeurIPS 上 [1]， 此篇论文开启了深度迁移学习的先河，非常值得一读。 该论文是一篇实验性研究相关的论文，全文都在做实验，并没有提出一种巧妙的算法。 Nov 6, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Deep networks, and in particular convolutional neural network (CNN) have also achieved many state-of-the-art competition results in the intelligent fault diagnosis of mechanical systems. Overview Keywords: Transfer Learning, Deep Neural Networks, Feature Transferability, Convolutional Networks, Generalization Objective: Quantify the generality versus specificity of features learned in deep neural networks across different layers. %0 Conference Paper %1 yosinski2014transferable %A Yosinski, Jason %A Clune, Jeff %A Bengio, Yoshua %A Lipson, Hod %B Advances in neural information processing systems %D 2014 %K %P 3320--3328 %T How transferable are features in deep neural networks? Modern deep neural networks exhibit a curious phenomenon: when trained on images, they all tend to learn first-layer features that resemble either Gabor filters or color blobs. Many deep neural networks trained on natural images exhibit a curious phe- nomenon in common: on the rst layer they learn features similar to Gabor lters and color blobs. Most applied DTL techniques are network/model-based approaches. Nov 6, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Features must eventually transition from general to specific by the Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Deep transfer learning, on the other hand, specifically refers to the use of deep neural networks (DNNs) in transfer learning. Deep neural networks (DNNs) excel at learning representations when trained on large-scale datasets. Among other techniques, this challenge can be tackled by Transfer Learning, which consists in re-using the knowledge previously learned by a model: this method is widely used and has proven effective in enhancing the performance While deep neural networks are more powerful for learning general and transferable features, the latest findings also re-veal that the deep features must eventually transition from general to specific along the network, and feature transfer-ability drops significantly in higher layers with increasing domain discrepancy. Oct 1, 2019 · To adapt the pre-trained CNN in a specific case, three transfer learning strategies are discussed and compared to investigate the applicability as well as the significance of feature transferability from the different levels of a deep structure. edu Dec 8, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. , 2015) compared with traditional methods. Features must eventually transition from general to specific Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer Abstract Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. Bengio, and H. The basic structure and principle are introduced. 2014 2014 [ArXiv] Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. The transfer network AnB investigates transferability of blocks between different tasks. Features must eventually transition from general to specific Abstract Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Yosinski, J. cc Nov 6, 2014 · In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. On their respective first layers, they learn features that are very similar Jun 4, 2019 · Motivation: Deep neural networks exhibits a common phenomenon: the first few layers learned by the network are features similar to Gabor filters. Lipson. Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor Explore with advanced AI tools for machine learning research. However, as deep features eventually transition from gen While deep neural networks are more powerful for learning general and transferable features, the latest findings also reveal that the deep features must eventually transition from general to specific along the network, and feature transferability drops significantly in higher layers with increasing domain discrepancy. Abstract Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. Read the article Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions on R Discovery, your go-to avenue for effective literature search. Jan 21, 2021 · Bibliographic details on How transferable are features in deep neural networks? How transferable are features in deep neural networks? Abstract: Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Adversarial Transfer Learning ¶ How transferable are features in deep neural networks? (NIPS 2014) Example: Given labelled grey-scaled MNIST and unlabeled color MNIST, want to train model for classifier of color MNIST without labelling color MNIST. See full list on papers. The lower layers of these networks learn general features like edges, shapes, and textures that are useful across many different vision tasks. Advances in Neural Information Processing Systems 27, pages 3320-3328. Transfer learning there-fore becomes even more important to neural mod-els. How transferable are features in deep neural networks? J. 3k 阅读 Recently, deep neural networks are emerging as the prevailing technical solution to almost every field in NLP. Hypothesis: The transferability of features in deep neural networks varies by layer, with lower layers being more general and higher layers more A new Deep Adaptation Network (DAN) architecture is proposed, which generalizes deep convolutional neural network to the domain adaptation scenario and can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Features must eventually transition from general to specific by the Sep 5, 2018 · Recent studies reveal that deep neural networks can learn transferable features that generalize well to similar novel tasks. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27 (NIPS '14), NIPS Foundation, 2014. Features must eventually transition from general to specic by the last layer Many deep neural networks trained on natural images exhibit a curious phe- nomenon in common: on the rst layer they learn features similar to Gabor lters and color blobs. Features must eventually transition from general to specific by the . Abstract Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. How transferable are features in deep neural networks ? Induction deep neural networks' curious pheomenon based on CNN : 在 image 上训练时，第一层学习到的特征通常类似于 Gabor filters 或 clor blobs，且在不同 datasets，不同 training objectives 上都会出现 Abstract Deep neural networks trained on large-scale dataset can learn transferable features that promote learning multiple tasks for inductive transfer and labeling mitigation. Hence, it is important to formally reduce the dataset bias and Supporting: 4, Mentioning: 278 - Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Features must eventually transition from general to specific Jul 6, 2015 · Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. Features must eventually transition from general to specific by the Jan 1, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. How transferable are features in deep neural networks? By: Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson Presentation by Anthony Chen antchen@ucdavis. Feb 23, 2023 · 论文笔记：How transferable are features in deep neural networks? 2014年NIP文章 原创 已于 2023-02-23 09:17:19 修改 · 1. Deep networks, and in particular convolutional neural network (CNN) have also achieved many state-of-the-art competition results in the intelligent fault diagnosis of mechanical system … Oct 24, 2024 · Paper Reading: "How Transferable Are Features in Deep Neural Networks?" | CS-505 Data Mining In this video, we present a detailed reading and discussion of the paper "How Transferable Are Features Abstract Many deep neural networks trained on natural images exhibit a curious phe-nomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Abstract: Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. While deep neural networks are more powerful for learning general and transferable features, the latest findings also re-veal that the deep features must eventually transition from general to specific along the network, and feature transfer-ability drops significantly in higher layers with increasing domain discrepancy. Disentangling explanatory factors of variations, DNNs are able to learn more transferable features [24, 3, 44, 47]. However, as deep features eventually transition from general to specific along the network, feature transferability drops significantly in higher task-specific layers with increasing domain discrepancy. Wether the transition for the first to second part is smoothed over the whole network or is sharp between two layers has not been studied yet. Groundbreaking research from Cornell, Wyoming, and Montreal reveals a bizarre pattern: virtually all deep neural networks start by learning identical low-level features, regardless of their Nov 6, 2014 · In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. It reports on the factors that affect transferability, such as task distance, optimization difficulties, and initialization effects. nips. Features must eventually transition from general to specic by the last layer Oct 1, 2019 · Recent years have witnessed increasing popularity and development of deep learning spanning through various fields. Nov 10, 2014 · Many deep neural networks trained on natural images exhibit a curious phenomenon: they all learn roughly the same Gabor filters and color blobs on the first layer. Features must eventually transition Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning. As deep features eventually transition from general to specific along the network, a fundamental problem is how to exploit the relationship structure across different tasks while accounting for the feature Abstract Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Nov 6, 2014 · A paper that experimentsally quantifies the generality versus specificity of neurons in each layer of a deep convolutional neural network. Features must eventually transition from general to specific Jul 6, 2025 · In deep learning, transfer learning works by taking a pre-trained neural network—typically trained on massive datasets like ImageNet—and adapting it for a specific task. We would like to show you a description here but the site won’t allow us. Pre-trained DNNs also show strong transferability when fine-tuned to other labeled datasets. Yosinski et al. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. To this end, the authors take an existing network (Krizhevsky et al. [2014], we How transferable are features in deep neural networks? (2014), J. Features must eventually transition from general to specific View recent discussion. Clune, Y. 2012), and performs generalization by fixing different layer depth and by transferring between different splits of How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson. This paper studies the transferability of features learnt at different layers of a convolutional neural network. Feb 9, 2015 · A new Deep Adaptation Network (DAN) architecture is proposed, which generalizes deep convolutional neural network to the domain adaptation scenario and can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. However, such transferability becomes weak when the target dataset is fully unlabeled as in Unsupervised Domain Adaptation (UDA). Advances in Neural Information Processing Systems, (2014 ) Mar 25, 2019 · Article on Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions, published in ISA Transactions 93 on 2019-03-25 by Te Han+3. — How transferable are features in deep neural networks? This form of transfer learning used in deep learning is called inductive transfer. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Features must eventually transition A new Deep Adaptation Network (DAN) architecture is proposed, which generalizes deep convolutional neural network to the domain adaptation scenario and can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. hiplc sjrkm nwsqt dvoif ksmrnl xnmgu eebxyn qfsi iaq eokkh kmxgibv vmxg igqaev xuq jwrkgjq