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 5826–5850 of 10307 papers

TitleStatusHype
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation—0
Synonym Expansion for Large Shopping Taxonomies—0
Synonyms, Antonyms and Beyond—0
Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion Extraction—0
Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Synthetic Image Data for Deep Learning—0
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications—0
T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task—0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network—0
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces—0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
Tag that issue: Applying API-domain labels in issue tracking systems—0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System—0
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation—0
Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection—0
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions—0
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
Tao: Re-Thinking DL-based Microarchitecture Simulation—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