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 4726–4750 of 10307 papers

TitleStatusHype
On the impact of measure pre-conditionings on general parametric ML models and transfer learning via domain adaptation—0
On the Intrinsic Limits to Representationally-Adaptive Machine-Learning—0
On the Limits of Learning Representations with Label-Based Supervision—0
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline—0
On the low-shot transferability of [V]-Mamba—0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning—0
On the Robustness of Arabic Speech Dialect Identification—0
On the Role of Neural Collapse in Transfer Learning—0
On the Role of Parallel Data in Cross-lingual Transfer Learning—0
On the safety of vulnerable road users by cyclist orientation detection using Deep Learning—0
On the Steganographic Capacity of Selected Learning Models—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
On the topology and geometry of population-based SHM—0
On The Transferability of Deep-Q Networks—0
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages—0
On the Transferability of Representations in Neural Networks Between Datasets and Tasks—0
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision—0
On the Transfer of Knowledge in Quantum Algorithms—0
On the universality of neural encodings in CNNs—0
Analysis of Knowledge Transfer in Kernel Regime—0
On the Usability of Transformers-based models for a French Question-Answering task—0
On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels—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