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 5951–5975 of 10307 papers

TitleStatusHype
Hierarchical Neural Network Approaches for Long Document Classification—0
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI—0
Optimizing Active Learning for Low Annotation Budgets—0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration—0
Growing Neural Network with Shared ParameterCode0
Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis—0
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus—0
When More is not Necessary Better: Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection Models—0
When does Parameter-Efficient Transfer Learning Work for Machine Translation?—0
XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction—0
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision—0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
ProQA: Structural Prompt-based Pre-training for Unified Question Answering—0
Multi-way VNMT for UGC: Improving Robustness and Capacity via Mixture Density Networks—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
Cross-lingual Lifelong Learning—0
Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferringCode0
Cooperative Self-training of Machine Reading Comprehension—0
An Empirical Study on Cross-Lingual and Cross-Domain Transfer for Legal Judgment Prediction—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—0
On Transferability of Prompt Tuning for Natural Language Processing—0
Exploring the Low-Resource Transfer-Learning with mT5 model—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering—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