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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7001–7025 of 10307 papers

TitleStatusHype
Spectro-Temporal RF Identification using Deep Learning—0
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer—0
Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios—0
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus—0
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network—0
Speech Synthesis for Low Resource Languages using Transliteration Enabled Transfer Learning—0
Speech Tasks Relevant to Sleepiness Determined with Deep Transfer Learning—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning—0
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23—0
Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning—0
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples—0
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
BreakingNews: Article Annotation by Image and Text Processing—0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—0
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data—0
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
Spoiler in a Textstack: How Much Can Transformers Help?—0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection—0
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—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