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 6226–6250 of 10307 papers

TitleStatusHype
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
ProQA: Structural Prompt-based Pre-training for Unified Question Answering—0
ProRuka: A highly efficient HMI algorithm for controlling a novel prosthetic hand with 6-DOF using sonomyography—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Baselines for Reinforcement Learning in Text Games—0
Prototype-Guided Memory Replay for Continual Learning—0
Prototypical Contrastive Transfer Learning for Multimodal Language Understanding—0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data—0
Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning—0
Provable benefits of representation learning—0
Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Models—0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Provably Robust Transfer—0
Proxy-informed Bayesian transfer learning with unknown sources—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Prune Once for All: Sparse Pre-Trained Language Models—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
Activity Recognition and Prediction in Real Homes—0
Actor Critic with Differentially Private Critic—0
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks—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