Deep Learning Investigation
🔍 1. Neural Network Architecture Exploration
Description:
Investigate various neural network architectures (CNNs, RNNs, LSTMs, Transformers) to understand their performance across different tasks such as image classification, language modeling, or time-series forecasting.
🧠 2. Interpretability and Explainability
Description:
Study methods like SHAP, LIME, and attention visualization to interpret deep learning models. Focus on making black-box models more transparent, especially in high-stakes applications (e.g., healthcare, finance).
🔄 3. Transfer Learning and Fine-Tuning
Description:
Explore how pre-trained models (like BERT, ResNet, or CLIP) can be fine-tuned for specific downstream tasks. Evaluate performance gain and reduction in training time/data requirements.
🧪 4. Adversarial Robustness
Description:
Investigate how deep learning models respond to adversarial attacks (e.g., FGSM, PGD). Explore defense mechanisms such as adversarial training, input preprocessing, and certified robustness.
🌍 5. Deep Learning for Sustainability
Description:
Examine how deep learning can optimize energy usage, predict climate patterns, or enhance smart grid systems. Investigate model efficiency and environmental impact (carbon footprint).
🧬 6. Deep Learning in Biomedical Imaging
Description:
Apply DL to tasks like tumor detection, organ segmentation, or disease classification in MRI, CT, and X-ray images. Use datasets like NIH ChestX-ray14 or BraTS for experiments.
🧾 7. Model Compression and Optimization
Description:
Study methods such as pruning, quantization, and knowledge distillation to make deep networks lightweight and faster, especially for edge devices or mobile deployment.
🤖 8. Reinforcement Learning with Deep Networks
Description:
Investigate how deep neural networks are integrated into reinforcement learning frameworks (e.g., DQN, PPO) for applications in robotics, games, and autonomous navigation.
📚 9. Dataset Bias and Ethical Implications
Description:
Explore how biases in training datasets affect DL model predictions. Study fairness metrics, bias mitigation techniques, and responsible AI practices.
🧠 10. Self-Supervised and Unsupervised Deep Learning
Description:
Investigate frameworks like SimCLR, BYOL, and Autoencoders to understand learning representations without labeled data. Focus on applications in low-resource environments.
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