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 3726–3750 of 10307 papers

TitleStatusHype
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations—0
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media—0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
Explicit Connection Distillation—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Automatically Score Tissue Images Like a Pathologist by Transfer Learning—0
Explicit Induction Bias for Transfer Learning with Convolutional Networks—0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation—0
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?—0
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation—0
Exploiting CNNs for Semantic Segmentation with Pascal VOC—0
Exploiting Convolutional Representations for Multiscale Human Settlement Detection—0
Exploiting Convolution Filter Patterns for Transfer Learning—0
A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
CNN Based Flank Predictor for Quadruped Animal Species—0
Exploiting Social Tags for Cross-Domain Collaborative Filtering—0
Federated Learning without Full Labels: A Survey—0
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction—0
Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—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