Channel Wise

"Channel-wise" processing in machine learning focuses on analyzing and manipulating data features across different channels (e.g., RGB channels in images, time series in sensor data), aiming to improve model performance and efficiency. Current research emphasizes developing methods that effectively balance channel independence (robustness) and channel dependence (expressivity) using techniques like low-rank adaptation, attention mechanisms (e.g., spatially autocorrelated attention), and state space models. These advancements are impacting diverse fields, including time series forecasting, image processing, and wireless communication, by enabling more accurate predictions, improved feature representation, and reduced computational overhead.