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 9601–9650 of 10307 papers

TitleStatusHype
Effects of Additional Data on Bayesian Clustering—0
Effects of Language Relatedness for Cross-lingual Transfer Learning in Character-Based Language Models—0
Effects of Soft-Domain Transfer and Named Entity Information on Deception Detection—0
Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification—0
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging—0
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift—0
Efficient Architecture Search for Continual Learning—0
Efficient Argument Structure Extraction with Transfer Learning and Active Learning—0
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
Efficient Conditional Pre-training for Transfer Learning—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Efficient Deployment of Deep MIMO Detection Using Learngene—0
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations—0
Efficient Domain Adaptation for Speech Foundation Models—0
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Efficient Graph Neural Architecture Search—0
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
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
Efficient Low-Resolution Face Recognition via Bridge Distillation—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Efficiently Robustify Pre-trained Models—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
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection—0
Efficient Neural Task Adaptation by Maximum Entropy Initialization—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning—0
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space—0
Efficient Task Transfer for HLS DSE—0
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks—0
Elastic Information Bottleneck—0
ELCC: the Emergent Language Corpus Collection—0
Electron-nucleus cross sections from transfer learning—0
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—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