SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 2501–2550 of 10307 papers

TitleStatusHype
Towards interpretable-by-design deep learning algorithms—0
Open Set Dandelion Network for IoT Intrusion Detection—0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names—0
Towards Robust and Accurate Visual Prompting—0
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer LearningCode4
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representationCode0
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint—0
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces—0
Facilitating the sharing of electrophysiology data analysis results through in-depth provenance captureCode0
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach—0
Language Semantic Graph Guided Data-Efficient LearningCode0
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language ModelsCode0
Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context LearningCode0
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation—0
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models—0
Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual AlignmentCode0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
Histopathologic Cancer DetectionCode0
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning—0
VGSG: Vision-Guided Semantic-Group Network for Text-based Person Search—0
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation—0
Developing a Named Entity Recognition Dataset for TagalogCode1
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots—0
C-Procgen: Empowering Procgen with Controllable Contexts—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models—0
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models—0
Transfer Learning for Structured Pruning under Limited Task Data—0
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree TransformationCode0
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification—0
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images—0
CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture—0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency—0
Enhancing Instance-Level Image Classification with Set-Level Labels—0
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related LanguagesCode0
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures—0
Generalization in medical AI: a perspective on developing scalable models—0
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic TopologyCode0
Transfer learning from a sparsely annotated dataset of 3D medical imagesCode1
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified