Transfer Learning
Transfer learning involves leveraging knowledge gained from a pre-trained model on one task to improve the performance on a different, but related task. This is particularly useful in situations where data for the target task is scarce.
CLIP
A model that learns to associate images and text by training on a large dataset of image-text pairs. It enables powerful zero-shot learning capabilities for various vision-language tasks.
Meta-learning
Meta-learning, or “learning to learn,” focuses on training models to quickly adapt to new tasks with limited data by identifying patterns across multiple tasks.
PEFT
Parameter-Efficient Fine-Tuning (PEFT) refers to techniques that adapt large pre-trained models by updating only a small subset of parameters, reducing computational costs and memory usage while retaining performance.
Continual Learning
Continual learning allows models to learn continuously from new data without forgetting previously acquired knowledge, addressing the issue of catastrophic forgetting.
Test-time Adaptation
Test-time adaptation refers to adapting a pre-trained model during inference, using only the input data to adjust the model without retraining, allowing the model to generalize better to new, unseen environments or tasks.
Others
Additional methods or extensions within transfer learning, such as domain adaptation and few-shot learning, which focus on further improving generalization across diverse scenarios.
Representation Learning
Representation learning focuses on automatically discovering useful features or representations from raw data, often for the purpose of improving downstream tasks such as classification or prediction.
Multi-modal Learning
Multi-modal learning integrates and processes data from multiple modalities (e.g., text, images, audio) to improve understanding and predictions, enabling models to make use of complementary information from different data types.
Self-supervised Learning
Self-supervised learning allows models to learn from unlabeled data by generating pseudo-labels, enabling efficient learning without requiring extensive human-annotated datasets.
Object-centric Learning
Object-centric learning emphasizes learning representations based on objects in the environment, enabling models to focus on key objects and their interactions, which is useful for tasks like scene understanding and manipulation.
Others
Techniques related to representation learning, such as contrastive learning, which identifies meaningful differences between data points.
Time-series Representation
Time-series representation learning aims to capture meaningful patterns and temporal dependencies in sequential data, allowing for better predictions and analyses in applications like forecasting and anomaly detection.
Real-world Applications
Applying the research methods and models to real-world tasks and challenges, translating theoretical insights into practical solutions.
Low-resource Scenarios
Low-resource applications involve adapting machine learning models to work effectively in scenarios with limited data or computational resources, often requiring creative techniques like transfer learning or data augmentation.
Open-set Real-world Scenarios
In open-set scenarios, models must handle unknown or unseen classes during inference, necessitating robust handling of unfamiliar inputs.
Imbalanced and Long-tailed Distributions
Scenarios involve datasets where certain classes are underrepresented, requiring specialized techniques to avoid biased predictions and improve generalization.
Distribution Shift
Distribution Shift referes to scenarios where the data distribution in deployment differs from the training data, requiring models to adapt to maintain performance.
LLM without Hallucination
This focuses on reducing hallucination in large language models, ensuring their outputs are factually accurate and grounded in reliable sources.
Others
Additional application challenges such as personalization, robustness, and fairness, which are critical for deploying models effectively in real-world settings.
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