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 1476–1500 of 10307 papers

TitleStatusHype
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled ApproachCode0
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads—0
Multi-Label Plant Species Classification with Self-Supervised Vision TransformersCode1
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression RecognitionCode1
Transfer Learning with Self-Supervised Vision Transformers for Snake IdentificationCode0
HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient TuningCode2
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients—0
Recent Advancements and Challenges of Turkic Central Asian Language Processing—0
CBM: Curriculum by MaskingCode0
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates—0
Understanding the Role of Invariance in Transfer LearningCode0
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASRCode0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
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
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual FeaturesCode0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
Impact of Financial Literacy on Investment Decisions and Stock Market Participation using Extreme Learning Machines—0
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
Knowledge Transfer with Simulated Inter-Image Erasing for Weakly Supervised Semantic SegmentationCode1
Knowledge Composition using Task Vectors with Learned Anisotropic ScalingCode1
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—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