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 4051–4075 of 10307 papers

TitleStatusHype
Few-Shot Nested Named Entity Recognition—0
A multilingual training strategy for low resource Text to Speech—0
Few-Shot Object Detection via Knowledge Transfer—0
Few-Shot Object Detection with Sparse Context Transformers—0
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Conditional Neural Processes for Molecules—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary—0
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfer—0
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation—0
Filtered Inner Product Projection for Crosslingual Embedding Alignment—0
Filtered Manifold Alignment—0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—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