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 8951–8975 of 10307 papers

TitleStatusHype
Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource LanguagesCode0
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer LearningCode0
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationCode0
Cats or CAT scans: transfer learning from natural or medical image source datasets?Code0
CaT: Weakly Supervised Object Detection with Category TransferCode0
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance LearningCode0
Causally Abstracted Multi-armed BanditsCode0
Bayesian Meta-Learning for Improving Generalizability of Health Prediction Models With Similar Causal MechanismsCode0
CBM: Curriculum by MaskingCode0
CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound ImagesCode0
Celebrity ProfilingCode0
Cell reprogramming design by transfer learning of functional transcriptional networksCode0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource LimitationsCode0
Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph CompletionCode0
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal DataCode0
Know Thy Strengths: Comprehensive Dialogue State Tracking DiagnosticsCode0
Chest X-Ray Images Classification with CNNCode0
Choice Fusion as Knowledge for Zero-Shot Dialogue State TrackingCode0
CIFAR-10 Image Classification Using Feature EnsemblesCode0
CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban ComputingCode0
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban DataCode0
CIZSL++: Creativity Inspired Generative Zero-Shot LearningCode0
CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected EnvironmentCode0
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer LearningCode0
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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