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 2876–2900 of 10307 papers

TitleStatusHype
Robust Adaptation of Foundation Models with Black-Box Visual Prompting—0
A Computer Vision Approach to Estimate the Localized Sea State—0
ELCC: the Emergent Language Corpus Collection—0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual FeaturesCode0
Impact of Financial Literacy on Investment Decisions and Stock Market Participation using Extreme Learning Machines—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations—0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering—0
Towards Training Music Taggers on Synthetic DataCode0
Soft Language Prompts for Language TransferCode0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Deep Image-to-Recipe TranslationCode0
Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models—0
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation—0
PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection—0
Cross-Lingual Transfer Learning for Speech Translation—0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
M^2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension—0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
A Deep Generative Framework for Joint Households and Individuals Population Synthesis—0
Image Classification for Snow Detection to Improve Pedestrian Safety—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