Direct Answer In deep learning, manual feature extraction is generally not required because neural networks automatically learn relevant features from raw data. Traditional machine learning methods rely on human experts to design features (like edge detectors for images or n-grams for text), but deep learning models such as convolutional neural networks (CNNs) or transformers ingest raw inputs (pixels, tokens) and derive hierarchical representations through training. For example, a CNN trained on images will learn low-level features like edges in early layers and high-level concepts like object shapes in deeper layers. This automation simplifies workflows, especially for complex data types like images, audio, or unstructured text. However, there are cases where domain-specific preprocessing or feature engineering can still enhance performance, particularly when data is limited or the problem has unique constraints.
When Feature Extraction Is Less Critical Deep learning excels in scenarios where data is abundant and the task aligns with common use cases like image classification, speech recognition, or natural language processing. For instance, models like ResNet or Vision Transformers (ViTs) achieve state-of-the-art results on ImageNet by processing raw pixel data without manual feature design. Similarly, language models like BERT process raw text tokens to capture semantic relationships. The key advantage is that these models discover patterns that might be unintuitive or labor-intensive for humans to engineer. Data augmentation (e.g., cropping, rotating images) further reduces the need for manual feature engineering by artificially expanding the training set. While preprocessing steps like normalization or tokenization are still applied, they are minimal compared to traditional methods and focus on standardizing input formats rather than extracting domain-specific features.
Exceptions and Practical Considerations Despite the automation, there are cases where feature extraction remains useful. For small datasets, manually engineered features can compensate for limited training data. For example, in medical imaging with scarce labeled examples, combining CNNs with precomputed features like texture metrics or histogram of gradients (HOG) might improve robustness. Similarly, in time-series forecasting, domain-specific features (rolling averages, Fourier transforms) can augment raw sensor data before feeding it into a recurrent neural network (RNN). Additionally, some tasks require interpretability: a fraud detection system might combine learned features from a neural network with manually defined rules for auditability. Even in deep learning pipelines, low-level preprocessing (e.g., converting audio to spectrograms for speech models) is a form of feature extraction. Thus, while deep learning minimizes manual effort, practitioners still balance automation with domain knowledge to optimize performance.