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

Showing 34263450 of 10307 papers

TitleStatusHype
TinySubNets: An efficient and low capacity continual learning strategyCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
TMI! Finetuned Models Leak Private Information from their Pretraining DataCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural NetworksCode0
Embedding neurophysiological signalsCode0
Embedding Ordinality to Binary Loss Function for Improving Solar Flare ForecastingCode0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Constrained Decoding for Cross-lingual Label ProjectionCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Towards Alzheimer's Disease Classification through Transfer LearningCode0
Celebrity ProfilingCode0
Emoji-Based Transfer Learning for Sentiment TasksCode0
Empower Sequence Labeling with Task-Aware Neural Language ModelCode0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classificationCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified