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 3376–3400 of 10307 papers

TitleStatusHype
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Efficient Integration of Multi-channel Information for Speaker-independent Speech Separation—0
Efficient keyword spotting using time delay neural networks—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
Efficient Learning of Less Biased Models with Transfer Learning—0
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—0
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Efficiently Robustify Pre-trained Models—0
A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning—0
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Evaluating the structure of cognitive tasks with transfer learning—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Deep Feature Rotation for Multimodal Image Style Transfer—0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning—0
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—0
Deep Feature Learning for Graphs—0
Efficient Task Transfer for HLS DSE—0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—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