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 6401–6425 of 10307 papers

TitleStatusHype
Improving Quality Control Of MRI Images Using Synthetic Motion Data—0
Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems—0
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition—0
Tensor Representation and Manifold Learning Methods for Remote Sensing Images—0
Improving Satellite Imagery Masking using Multi-task and Transfer Learning—0
Semantic Parsing in Limited Resource Conditions—0
Improving Sentence Boundary Detection for Spoken Language Transcripts—0
Improving Signer Independent Sign Language Recognition for Low Resource Languages—0
Semantic Pose using Deep Networks Trained on Synthetic RGB-D—0
Improving Similar Language Translation With Transfer Learning—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving speaker turn embedding by crossmodal transfer learning from face embedding—0
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning—0
Improving speech recognition models with small samples for air traffic control systems—0
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
Semantic Preserving Generative Adversarial Models—0
Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features—0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—0
Improving the Language Model for Low-Resource ASR with Online Text Corpora—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