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 9001–9050 of 10307 papers

TitleStatusHype
Cyclegan Network for Sheet Metal Welding Drawing Translation—0
CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion—0
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems—0
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks—0
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
Damage detection using in-domain and cross-domain transfer learning—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning—0
DaNetQA: a yes/no Question Answering Dataset for the Russian Language—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
DARE: A large-scale handwritten date recognition system—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
Dark Reciprocal-Rank: Boosting Graph-Convolutional Self-Localization Network via Teacher-to-student Knowledge Transfer—0
DARTS: Dialectal Arabic Transcription System—0
DASGrad: Double Adaptive Stochastic Gradient—0
Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican—0
Data-adaptive Transfer Learning for Low-resource Translation: A Case Study in Haitian—0
Data Annealing for Informal Language Understanding Tasks—0
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition—0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Data augmentation with mixtures of max-entropy transformations for filling-level classification—0
Data-Centric AI in the Age of Large Language Models—0
Data-driven Approaches to Surrogate Machine Learning Model Development—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Data-driven geophysics: from dictionary learning to deep learning—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles—0
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning—0
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach—0
Data-Driven Transfer Learning Framework for Estimating Turning Movement Counts—0
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning—0
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
Data-Efficient Information Extraction from Form-Like Documents—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
Data-Efficient Methods for Dialogue Systems—0
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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