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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3626–3650 of 10307 papers

TitleStatusHype
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Weight subcloning: direct initialization of transformers using larger pretrained ones—0
Optimizing Mario Adventures in a Constrained EnvironmentCode0
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge TransferCode0
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-LearningCode0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions—0
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models—0
Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Automated Behavioral Analysis Using Instance SegmentationCode0
Understanding and Leveraging the Learning Phases of Neural Networks—0
Initialization Matters for Adversarial Transfer LearningCode0
Jumpstarting Surgical Computer Vision—0
Facial Beauty Analysis Using Distribution Prediction and CNN EnsemblesCode0
Hacking Task Confounder in Meta-LearningCode0
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer—0
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images—0
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-IdentificationCode0
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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