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Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Multitask Learning
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Visualizing and Understanding Convolutional Networks
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Microsoft COCO: Common Objects in Context
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The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
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Identity Mappings in Deep Residual Networks
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Local Alignments for Fine-Grained Categorization
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ImageNet Large Scale Visual Recognition Challenge
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Deep Filter Banks for Texture Recognition, Description, and Segmentation
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Texture features for browsing and retrieval of image data
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Recognizing indoor scenes
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Locality-constrained Linear Coding for image classification
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Multipath Sparse Coding Using Hierarchical Matching Pursuit
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Transformation Pursuit for Image Classification
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
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Hyper-class augmented and regularized deep learning for fine-grained image classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
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Fine-grained visual categorization via multi-stage metric learning
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Understanding deep image representations by inverting them
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Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
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Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework
inferredcites →https://doi.org/10.1109/cvpr.2016.68openalex-snapshot
Scene Recognition with CNNs: Objects, Scales and Dataset Bias
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvprw.2014.131openalex-snapshot
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
inferredcites →https://doi.org/10.1109/cvprw.2015.7301270openalex-snapshot
From generic to specific deep representations for visual recognition
inferredcites →https://doi.org/10.1109/cvprw.2015.7301274openalex-snapshot
Multi-scale pyramid pooling for deep convolutional representation
inferredcites →https://doi.org/10.1109/iccv.2015.136openalex-snapshot
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
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Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
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Bilinear CNN Models for Fine-Grained Visual Recognition
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Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
inferredcites →https://doi.org/10.1109/iccv.2015.314openalex-snapshot
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.463openalex-snapshot
Simultaneous Deep Transfer Across Domains and Tasks
inferredcites →https://doi.org/10.1109/icvgip.2008.47openalex-snapshot
Automated Flower Classification over a Large Number of Classes
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A Survey on Transfer Learning
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Visual Event Recognition in Videos by Learning from Web Data
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Caffe
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ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.48550/arxiv.1310.1531openalex-snapshot
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1411.1792openalex-snapshot
How transferable are features in deep neural networks?
inferredcites →https://doi.org/10.48550/arxiv.1502.02791openalex-snapshot
Learning Transferable Features with Deep Adaptation Networks
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Multi-Task Feature Learning
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Correcting Sample Selection Bias by Unlabeled Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Selective Adversarial Adaptation-Based Cross-Scene Change Detection Framework in Remote Sensing Images
inferred← citeshttps://doi.org/10.1109/tgrs.2020.3028223openalex-snapshot
Human Motion Recognition With Limited Radar Micro-Doppler Signatures
inferred← citeshttps://doi.org/10.1109/tgrs.2021.3090106openalex-snapshot
Semisupervised Human Activity Recognition With Radar Micro-Doppler Signatures
inferred← citeshttps://doi.org/10.1109/tgrs.2022.3229366openalex-snapshot
Multimodal Remote Sensing Image Matching Combining Learning Features and Delaunay Triangulation
inferred← citeshttps://doi.org/10.1109/tgrs.2025.3647787openalex-snapshot
PCIR: An Open-World Remote Sensing Image Representation Learning Method From a Causal Perspective
inferred← citeshttps://doi.org/10.1109/tifs.2023.3343193openalex-snapshot
A Class-Incremental Approach With Self-Training and Prototype Augmentation for Specific Emitter Identification
inferred← citeshttps://doi.org/10.1109/tim.2018.2863438openalex-snapshot
Deep Nearest Class Mean Model for Incremental Odor Classification
inferred← citeshttps://doi.org/10.1109/tim.2025.3529056openalex-snapshot
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification
inferred← citeshttps://doi.org/10.1109/tip.2019.2908795openalex-snapshot
Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification
inferred← citeshttps://doi.org/10.1109/tip.2020.2973812openalex-snapshot
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
inferred← citeshttps://doi.org/10.1109/tip.2022.3205215openalex-snapshot
SR-GNN: Spatial Relation-Aware Graph Neural Network for Fine-Grained Image Categorization
inferred← citeshttps://doi.org/10.1109/tip.2026.3712338openalex-snapshot
H 3 Former: Hypergraph-Based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3183326openalex-snapshot
Transfer Adaptation Learning: A Decade Survey
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3418527openalex-snapshot
Mask-Guided Vision Transformer for Few-Shot Learning
inferred← citeshttps://doi.org/10.1109/tnnls.2025.3588327openalex-snapshot
ODMTCNet: An Interpretable Multiview Deep Neural Network Architecture for Feature Representation
inferred← citeshttps://doi.org/10.1109/tpami.2021.3129870openalex-snapshot
Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types
inferred← citeshttps://doi.org/10.1109/tpds.2021.3055019openalex-snapshot
Optimizing Resource Allocation for Data-Parallel Jobs Via GCN-Based Prediction
inferred← citeshttps://doi.org/10.1109/tpsisa52974.2021.00013openalex-snapshot
Selective Targeted Transfer Learning for Malware Classification
inferred← citeshttps://doi.org/10.1109/tpwrs.2022.3220955openalex-snapshot
Bidirectional Active Transfer Learning for Adaptive Power System Stability Assessment and Dominant Instability Mode Identification
inferred← citeshttps://doi.org/10.1109/wacv.2019.00045openalex-snapshot
Instance-Based Deep Transfer Learning
inferred← citeshttps://doi.org/10.1117/12.2278862openalex-snapshot
Deep learning-based fine-grained car make/model classification for visual surveillance
inferred← citeshttps://doi.org/10.1134/s1054661820040197openalex-snapshot
Multi-View Facial Expression Recognition with Multi-View Facial Expression Light Weight Network
inferred← citeshttps://doi.org/10.1145/3123266.3123317openalex-snapshot
Multi-Networks Joint Learning for Large-Scale Cross-Modal Retrieval
inferred← citeshttps://doi.org/10.1145/3240508.3240574openalex-snapshot
Facial Expression Recognition in the Wild
inferred← citeshttps://doi.org/10.1145/3343031.3350874openalex-snapshot
AdaCompress
inferred← citeshttps://doi.org/10.1145/3343031.3351035openalex-snapshot
WealthAdapt
inferred← citeshttps://doi.org/10.1145/3343031.3351082openalex-snapshot
Fewer-Shots and Lower-Resolutions
inferred← citeshttps://doi.org/10.1145/3372278.3390724openalex-snapshot
Efficient Base Class Selection Algorithms for Few-Shot Classification
inferred← citeshttps://doi.org/10.1145/3379344openalex-snapshot
Knowledge Transfer in Vision Recognition
inferred← citeshttps://doi.org/10.1145/3447878openalex-snapshot
Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach
inferred← citeshttps://doi.org/10.1145/3448218.3448221openalex-snapshot
Identifying Chinese Herbal Medicine by Image with Three Deep CNNs
inferred← citeshttps://doi.org/10.1145/3473912openalex-snapshot
Knowledge Distillation with Attention for Deep Transfer Learning of Convolutional Networks
inferred← citeshttps://doi.org/10.1145/3530836openalex-snapshot
GrOD : Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training
inferred← citeshttps://doi.org/10.1155/2020/8873137openalex-snapshot
Learning Transferable Convolutional Proxy by SMI-Based Matching Technique
inferred← citeshttps://doi.org/10.1155/2020/8893381openalex-snapshot
Estimating Network Flowing over Edges by Recursive Network Embedding
inferred← citeshttps://doi.org/10.1155/2021/2518837openalex-snapshot
Deep Transfer Learning for Biology Cross-Domain Image Classification
inferred← citeshttps://doi.org/10.1162/tacl_a_00658openalex-snapshot
Simultaneous Selection and Adaptation of Source Data via Four-Level Optimization
inferred← citeshttps://doi.org/10.1186/s12879-024-09428-4openalex-snapshot
A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine
inferred← citeshttps://doi.org/10.12720/jait.17.4.636-647openalex-snapshot
A Metrics-Based Framework for Model Selection in Object Detection Systems
inferred← citeshttps://doi.org/10.14421/ijid.2020.09207openalex-snapshot
A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification
inferred← citeshttps://doi.org/10.14722/ndss.2020.24178openalex-snapshot
CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples
inferred← citeshttps://doi.org/10.1609/aaai.v34i04.5824openalex-snapshot
AdaFilter: Adaptive Filter Fine-Tuning for Deep Transfer Learning
inferred← citeshttps://doi.org/10.1609/aaai.v34i07.6712openalex-snapshot
Channel Interaction Networks for Fine-Grained Image Categorization
inferred← citeshttps://doi.org/10.1609/aaai.v35i2.16176openalex-snapshot
Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification
inferred← citeshttps://doi.org/10.1609/aaai.v37i1.25150openalex-snapshot
RankDNN: Learning to Rank for Few-Shot Learning
inferred← citeshttps://doi.org/10.1609/aaai.v37i2.25249openalex-snapshot
SEPT: Towards Scalable and Efficient Visual Pre-training
inferred← citeshttps://doi.org/10.18280/ts.400123openalex-snapshot
Towards Domain-Aware Transfer Learning for Medical Image Analysis: Opportunities and Challenges
inferred← citeshttps://doi.org/10.18653/v1/2022.ltedi-1.8openalex-snapshot
Debiasing Pre-Trained Language Models via Efficient Fine-Tuning
inferred← citeshttps://doi.org/10.20334/2022-033-mopenalex-snapshot
Improving the effectiveness of voice activation systems with machine learning methods
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1361817/v1openalex-snapshot
Image embeddings extracted from CNNs outperform other transfer learning approaches in chest radiographs’ classification
inferred← citeshttps://doi.org/10.21608/jaet.2023.142870.1184openalex-snapshot
Remote sensing Image processing, DEEP learning Classification algorithms survey and review
inferred← citeshttps://doi.org/10.23919/cisti.2018.8399417openalex-snapshot
A mobile application for the recognition of banknote: Analternative in the processing of images in people with visual disability
inferred← citeshttps://doi.org/10.3233/aic-230104openalex-snapshot
Predicting the success of transfer learning for genetic programming using DeepInsight feature space alignment
inferred← citeshttps://doi.org/10.3390/app11146298openalex-snapshot
Voice Activation for Low-Resource Languages
inferred← citeshttps://doi.org/10.3390/app11178097openalex-snapshot
Automatic Surgical Instrument Recognition—A Case of Comparison Study between the Faster R-CNN, Mask R-CNN, and Single-Shot Multi-Box Detectors
inferred← citeshttps://doi.org/10.3390/app14104005openalex-snapshot
SkinSwinViT: A Lightweight Transformer-Based Method for Multiclass Skin Lesion Classification with Enhanced Generalization Capabilities
inferred← citeshttps://doi.org/10.3390/bioengineering11010019openalex-snapshot
A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology
inferred← citeshttps://doi.org/10.3390/diagnostics12092084openalex-snapshot
Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs
inferred← citeshttps://doi.org/10.3390/electronics10101148openalex-snapshot
PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data
inferred← citeshttps://doi.org/10.3390/electronics11111752openalex-snapshot
Few-Shot Image Classification: Current Status and Research Trends
inferred← citeshttps://doi.org/10.3390/electronics12041054openalex-snapshot
Noise2Clean: Cross-Device Side-Channel Traces Denoising with Unsupervised Deep Learning
inferred← citeshttps://doi.org/10.3390/electronics12153221openalex-snapshot
A Two-Stage Image Inpainting Technique for Old Photographs Based on Transfer Learning
inferred← citeshttps://doi.org/10.3390/rs14174380openalex-snapshot
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data
inferred← citeshttps://doi.org/10.3390/s19163556openalex-snapshot
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
inferred← citeshttps://doi.org/10.3390/s19194139openalex-snapshot
Ensembles of Deep Learning Models and Transfer Learning for Ear Recognition
inferred← citeshttps://doi.org/10.3390/s24113522openalex-snapshot
Deep Transfer Learning Method Using Self-Pixel and Global Channel Attentive Regularization
inferred← citeshttps://doi.org/10.3390/sym11121493openalex-snapshot
Handcrafted versus CNN Features for Ear Recognition
inferred← citeshttps://doi.org/10.3390/w15112082openalex-snapshot
Intelligent Detection Method for Concrete Dam Surface Cracks Based on Two-Stage Transfer Learning
inferred← citeshttps://doi.org/10.36227/techrxiv.174906148.89641328/v1openalex-snapshot
Federated Learning for UAV Perception Task : A Survey
inferred← citeshttps://doi.org/10.3788/lop241334openalex-snapshot
基于多层递进引导的人脸图像修复网络
inferred← citeshttps://doi.org/10.4018/ijitsa.321133openalex-snapshot
Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments
inferred← citeshttps://doi.org/10.48550/arxiv.1707.07465openalex-snapshot
Building Graph Representations of Deep Vector Embeddings
inferred← citeshttps://doi.org/10.48550/arxiv.1709.02995openalex-snapshot
Optimal Transport for Deep Joint Transfer Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1711.09648openalex-snapshot
Transfer Learning in CNNs Using Filter-Trees
inferred← citeshttps://doi.org/10.48550/arxiv.1805.09622openalex-snapshot
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
inferred← citeshttps://doi.org/10.48550/arxiv.1901.09229openalex-snapshot
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1909.08148openalex-snapshot
AdaCompress: Adaptive Compression for Online Computer Vision Services
inferred← citeshttps://doi.org/10.48550/arxiv.1909.12031openalex-snapshot
Towards Understanding the Transferability of Deep Representations
inferred← citeshttps://doi.org/10.48550/arxiv.1911.01071openalex-snapshot
Supervised level-wise pretraining for recurrent neural network initialization in multi-class classification
inferred← citeshttps://doi.org/10.48550/arxiv.1912.08812openalex-snapshot
Research Frontiers in Transfer Learning -- a systematic and bibliometric review
inferred← citeshttps://doi.org/10.48550/arxiv.2001.04893openalex-snapshot
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders
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