Transfer Learning Unleashed: From Generating Neural Networks to Diagnosing Disease

Latest 38 papers on transfer learning: Jan. 10, 2026

Transfer learning has emerged as a powerhouse in modern AI/ML, enabling models to leverage knowledge gained from one task or domain to accelerate learning and improve performance on another. This approach is particularly transformative in scenarios where data is scarce, computational resources are limited, or rapid adaptation to new environments is crucial. Recent research highlights a fascinating array of advancements, pushing the boundaries of what transfer learning can achieve—from synthesizing entire neural networks to pinpointing medical conditions and even optimizing industrial processes.

The Big Idea(s) & Core Innovations

At the heart of these breakthroughs lies the ingenious reuse and adaptation of learned representations. One of the most audacious innovations comes from Saumya Gupta and their team at Northeastern University with DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights. This pioneering work uses Flow Matching (FM) to generate complete neural network weights for diverse architectures (MLP, ResNet, ViT, BERT) with up to 100 million parameters, all without requiring fine-tuning. They tackle challenges like weight space symmetries using canonicalization techniques, demonstrating superior efficiency and diversity over diffusion-based models.

In a similar vein of efficient adaptation, V. Martinek and R. Herzog from Heidelberg University introduce a novel approach in Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing. They enhance symbolic regression by incorporating fully-shared, partially-shared, and non-shared parameters for categorical variables. This reduces individual parameter count and data requirements while enabling better generalization and transfer across different category-value combinations.

Another significant theme is robust domain adaptation, particularly crucial when source and target domains differ substantially. Deniz Akdemir’s theoretical work, Le Cam Distortion: A Decision-Theoretic Framework for Robust Transfer Learning, identifies the “Invariance Trap” in traditional methods like UDA, where unequally informative domains lead to negative transfer. Their proposed directional simulability via Le Cam deficiency minimization offers a theoretically grounded, safer way to transfer knowledge without degrading the source utility, vital for safety-critical applications.

For time series data, Hana YAHIA and colleagues from Mines Paris, PSL University in Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction show that Adversarial Domain Generalization (ADG) and Invariant Risk Minimization (IRM) significantly boost the accuracy of predicting drilling stick-slip events. Crucially, they demonstrate that transfer learning further enhances performance on pre-trained models within this context.

In the realm of natural language processing, Amal Alqahtani et al. from The George Washington University present StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection. By leveraging data from related mental health conditions, StressRoBERTa achieves an impressive 82% F1 score in stress detection on English tweets, showcasing the power of cross-condition continual training for specialized NLP tasks.

The challenge of data scarcity is also addressed in Fadhil Muhammad et al.’s Stuttering-Aware Automatic Speech Recognition for Indonesian Language. They use synthetic data augmentation combined with fine-tuning a pre-trained Whisper model to significantly improve ASR performance on stuttered Indonesian speech, circumventing the need for extensive real-world disfluent data.

Under the Hood: Models, Datasets, & Benchmarks

These innovations are powered by sophisticated models and carefully curated data strategies:

• DeepWeightFlow: Generates weights for MLP, ResNet, ViT, and BERT architectures, with a public code repository at github.com/NNeuralDynamics/DeepWeightFlow.
• Custom CNNs vs. Pre-trained Architectures: Several papers (Training a Custom CNN on Five Heterogeneous Image Datasets, Performance Analysis of Image Classification on Bangladeshi Datasets, Comparative Analysis of Custom CNN Architectures versus Pre-trained Models and Transfer Learning: A Study on Five Bangladesh Datasets, A Comparative Study of Custom CNNs, Pre-trained Models, and Transfer Learning Across Multiple Visual Datasets, Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks) consistently evaluate and compare ResNet-18, ResNet-50, VGG-16, MobileNet, EfficientNetB0, MobileNetV2, and custom CNN designs on diverse datasets including FootpathVision, MangoImageBD, PaddyVarietyBD, Road Damage, and Smart City unauthorized vehicle detection (many specific to Bangladesh). These studies reveal that while custom CNNs offer computational efficiency, fine-tuned pre-trained models typically achieve superior accuracy, especially on limited or complex datasets. Mahmudul Hasan and Mabsur Fatin Bin Hossain also introduce MiniYOLO for lightweight object detection, with code available at github.com/MahmudulHasan/EvolvingCNNArchitectures.
• LoRA for Efficiency and Adaptation: Low-Rank Adaptation (LoRA) is featured prominently. Vikram Seenivasan et al. use LoRA in Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction for astrophysical tasks. Samar Khanna et al. introduce ExPLoRA (ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts) for efficient Vision Transformer (ViT) adaptation in satellite imagery, outperforming fully pre-trained models with minimal parameters. The concept is further explored in The Quest for Winning Tickets in Low-Rank Adapters, which extends the Lottery Ticket Hypothesis to LoRAs, showing significant parameter reduction through random masking, with code at github.com/hameddamirchi/partial-lora.
• Medical Imaging: For pediatric pneumonia detection, papers like Pediatric Pneumonia Detection from Chest X-Rays: A Comparative Study of Transfer Learning and Custom CNNs and Deep Learning Approach for the Diagnosis of Pediatric Pneumonia Using Chest X-ray Imaging leverage ResNet50, DenseNet121, EfficientNet-B0, ResNetRS, RegNet, and EfficientNetV2 models, often pre-trained on ImageNet. These studies emphasize stratified data splits and Grad-CAM for interpretability.
• Specialized Models: MambaFormer (MambaFormer: Token-Level Guided Routing Mixture-of-Experts for Accurate and Efficient Clinical Assistance) combines SSM and Transformer models for efficient clinical QA, fine-tuned on DentalQA and PubMedQA. KMINN (Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics) integrates Williams enrichment with physics-informed NNs for fracture mechanics. GAATNet (Graph Attention-based Adaptive Transfer Learning for Link Prediction) combines graph attention networks with transfer learning for link prediction, with code at github.com/DSI-Lab1/GAATNet.
• Generative AI for Data Augmentation: GaitMotion (Real-Time Forecasting of Pathological Gait via IMU Navigation: A Few-Shot and Generative Learning Framework for Wearable Devices) uses generative AI to augment rare pathological gait patterns for IMU data, improving stride length estimation. For stuttering ASR, synthetic data generation is used to fine-tune Whisper models.

Impact & The Road Ahead

These advancements herald a future where AI models are not only more accurate but also more adaptable, efficient, and interpretable. The ability to generate entire neural networks or effectively adapt pre-trained ones to new, challenging domains—whether it’s predicting mycotoxin contamination in oats (Predicting Mycotoxin Contamination in Irish Oats Using Deep and Transfer Learning), identifying hidden road defects (Intelligent recognition of GPR road hidden defect images based on feature fusion and attention mechanism), or even decoding imagined speech from EEG signals (EEG-to-Voice Decoding of Spoken and Imagined speech Using Non-Invasive EEG with code at github.com/pukyong-nu/eeg-to-voice)—opens up vast possibilities.

From healthcare, where AI can assist in early sepsis prediction using wearables (Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm) and enable personalized neuromorphic systems for EEG processing (Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing with code at github.com/NEO-ETHZ/EEG-Ferro), to industrial applications like optimizing manufacturing systems (Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems) and even automating stage lighting with generative models (Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task? with code at github.com/RS2002/Skip-BART), transfer learning is proving its mettle. The continued exploration of parameter-efficient methods like LoRA and robust theoretical frameworks like Le Cam Distortion will be key to unlocking even more potential, making AI more accessible and reliable across an ever-expanding range of real-world challenges. The future of AI is not just about building bigger models, but smarter, more adaptable ones.