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- Perceptual encoding Description: Perceptual coding is a technique that optimizes data representation based on human perception. This approach focuses on how humans(...) Read more
- Pixel-wise Loss Function Description: The 'Pixel-wise Loss Function' is a specific type of loss function used in the field of image generation, particularly within(...) Read more
- Pooling Operation Description: The pooling operation, also known as pooling, is a fundamental technique in convolutional neural networks (CNNs) used to reduce the(...) Read more
- Pixel-wise Description: Pixel-wise refers to operations or calculations that are performed on each pixel individually. This approach is fundamental in(...) Read more
- Patch-based Description: Patch-based refers to methods that analyze small regions (patches) of input data, rather than processing the entire image or(...) Read more
- Probabilistic Description: Probabilistic methods in the context of convolutional neural networks (CNNs) refer to approaches that utilize probability theory to(...) Read more
- Patch extraction Description: Patch extraction is the process of selecting small regions of an image for analysis. This approach is fundamental in the context of(...) Read more
- Pixel intensity Description: Pixel intensity refers to the brightness or color value of a pixel in an image. Each pixel in a digital image has a value that(...) Read more
- Pixel aggregation Description: Pixel aggregation is the process of combining pixel values to create a summarized representation of an image. This concept is(...) Read more
- Patch matching Description: Patch matching is a technique used in image processing that focuses on identifying and comparing small sections or 'patches' of(...) Read more
- Pixel segmentation Description: Pixel segmentation is the process of partitioning an image into segments based on pixel characteristics. This approach allows for(...) Read more
- Pre-training Description: Pre-training is the initial training phase of a large language model, where it is exposed to a vast dataset of textual information(...) Read more
- Post-training Description: Post-training refers to the phase that follows pre-training in the development of large language models (LLMs). During this stage,(...) Read more
- Prompt-based learning Description: Prompt-based learning is an innovative approach in the field of large language models that uses instructions or 'prompts' to guide(...) Read more
- Pre-trained embeddings Description: Pretrained embeddings are vector representations of words or phrases generated from a machine learning process where a large(...) Read more
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