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Transfer Learning

What is Transfer Learning? Transfer learning is a machine learning technique where an already trained model is utilized to solve a different but related problem. The concept of transfer learning is to leverage the knowledge gained from a previously trained algorithm to help another algorithm solve a related problem efficiently, quickly, and accurately. Transfer learning is a valuable tool for machine learning because it allows developers, researchers, and designers to train accurate models for

TransferQA

Overview of TransferQA TransferQA is a type of generative question-answering model that is designed to be transferable, meaning it can be applied to different types of data sets. It was built on top of T5, which is a type of transformer framework. A transformer is a special kind of learning algorithm that can process text data. It is particularly good at language modeling, which means it can understand and generate text more like humans do. T5 is a special kind of transformer that is particula

Transformer Decoder

The Transformer-Decoder (T-D) is a type of neural network architecture used for text generation and prediction. It is similar to the Transformer-Encoder-Decoder architecture but drops the encoder module, making it more lightweight and suited for longer sequences. What is a Transformer-Encoder-Decoder? The Transformer-Encoder-Decoder (TED) is a neural network architecture used for natural language processing tasks such as machine translation and text summarization. It was introduced in 2017 by

Transformer in Transformer

The topic of TNT is an innovative approach to computer vision technology that utilizes a self-attention-based neural network called Transformer to process both patch-level and pixel-level representations of images. This novel Transformer-iN-Transformer (TNT) model uses an outer transformer block to process patch embeddings and an inner transformer block to extract local features from pixel embeddings, thereby allowing for a more comprehensive view of the image features. Ultimately, the TNT model

Transformer-XL

What is Transformer-XL? Transformer-XL is a type of Transformer architecture that incorporates the notion of recurrence to the deep self-attention network. It is designed to model long sequences of text by reusing hidden states from previous segments, which serve as a memory for the current segment. This enables the model to establish connections between different segments and thus model long-term dependency more efficiently. How does it work? The Transformer-XL uses a new form of attention

Transformer

Transformers are a significant advancement in the field of artificial intelligence and machine learning. They are model architectures that rely on an attention mechanism instead of recurrence, unlike previous models based on recurrent or convolutional neural networks. The attention mechanism allows for global dependencies between input and output, resulting in better performance and more parallelization. What is a Transformer? A Transformer is a type of neural network architecture used for se

Transliteration

Overview of Transliteration Transliteration is a process of converting words from a source, foreign language to a target language. It is commonly used in cross-lingual information retrieval, information extraction, and machine translation. The primary objective of transliteration is to preserve the original pronunciation of the source word while following the phonological structures of the target language. It is different from machine translation, which focuses on preserving the semantic meanin

Transparent Object Depth Estimation

Transparent objects often pose a challenge when it comes to 3D shape estimation due to the lack of visual cues offered by conventional objects. This issue is particularly prominent in fields such as robotics, autonomous vehicles, and object recognition. Fortunately, experts have developed methods that allow for accurate and efficient estimation of the 3D shape and depth of transparent objects. What is transparent object depth estimation? Transparent object depth estimation refers to the abili

Tree Ensemble to Rules

TE2Rules: A Method to Make AI Models More Transparent What is TE2Rules? TE2Rules is a method used to convert a Tree Ensemble model, which is a type of artificial intelligence (AI) model used in machine learning, into a Rule list. Essentially, this process breaks down the complex decision-making processes employed by AI models into simple rules that can be easily understood and interpreted by humans. This makes it possible for humans to understand how a decision was reached and to identify any

TResNet

A TResNet is a variation of a ResNet that is designed to improve accuracy while maintaining efficient training and inference using a GPU. This type of network incorporates several design elements, including SpaceToDepth stem, Anti-Alias downsampling, In-Place Activated BatchNorm, Blocks selection, and squeeze-and-excitation layers to achieve its improved performance. ResNet Basics Before discussing TResNets, it’s important to understand the basics of ResNets. ResNets (short for residual netwo

TridentNet Block

Overview of TridentNet Block: The TridentNet Block is a feature extractor that is utilized in object detection models. Through this block, the backbone network adapts to different scales to generate multiple scale-specific feature maps. This is achieved by utilizing dilated convolutions, where the different branches of the trident block share the same network structure and parameters, but have different receptive fields. Understanding TridentNet Block: Object detection models are a type of c

TridentNet

TridentNet is a highly advanced and innovative object detection architecture that is designed to create scale-specific feature maps that have a uniform representational power. With its state-of-the-art structure and unique features, TridentNet has quickly become a highly popular solution for those seeking accurate and efficient object detection. The Basics of TridentNet Architecture The foundational aspect of TridentNet is a parallel multi-branch architecture, with each branch of the network

Triplet Attention

Understanding Triplet Attention Triplet Attention is a technique used in deep learning to improve the performance of convolutional neural networks, which are used for image recognition, object detection, and many other computer vision applications. It works by breaking down an input image into three parts or branches, each responsible for capturing a different type of information. The three branches of Triplet Attention are designed to capture cross-dimensional features between the spatial dim

Triplet Entropy Loss

Triplet Entropy Loss: Improving the Training Process In the field of machine learning, neural networks are trained using various methods to improve the accuracy and efficiency of the models. One of these methods is Triplet Entropy Loss (TEL), which combines the strengths of Cross Entropy Loss and Triplet loss to achieve better generalization. What is Triplet Entropy Loss? Before diving into Triplet Entropy Loss, it’s essential to understand Cross Entropy Loss and Triplet loss and how they ar

Triplet Loss

Overview of Triplet Loss in Siamese Networks Triplet loss is a method used in Siamese Networks to maximize the likelihood of positive score pairs while minimizing the likelihood of negative score pairs. In this context, the loss function is designed to produce a summary of the difference between embeddings for similar and dissimilar input pairs. This article will provide a brief overview of the triplet loss algorithm, its application in machine learning, and its benefits. What is Triplet Loss

TrIVD-GAN

TrIVD-GAN, or Transformation-based & TrIple Video Discriminator GAN, is a cutting-edge technology in the field of video generation that builds upon DVD-GAN. It has several improvements that make it more expressive and efficient as compared to its predecessor. With TrIVD-GAN, the generator of GAN is made more expressive by incorporating the TSRU (transformation-based recurrent unit), while the discriminator architecture is improved to make it more accurate. What is TrIVD-GAN? TrIVD-GAN is a ty

TrOCR

Overview of TrOCR TrOCR is a cutting-edge OCR (Optical Character Recognition) model that uses pre-trained models for both CV (Computer Vision) and NLP (Natural Language Processing) to recognize and generate text from images. It utilizes the Transformer architecture to decipher text from images at a wordpiece-level. The aim of this model is to streamline the process of reading scanned documents or images with text, converting the images into legible text for easy reading and indexing. How TrOC

True Online TD Lambda

True Online $TD(\lambda)$ is a machine learning algorithm that seeks to efficiently approximate the ideal online $\lambda$-return algorithm through the use of eligibility traces. It is a forward-looking algorithm that uses dutch traces instead of accumulating traces to create a more computational efficient backward-view algorithm. What is True Online $TD(\lambda)$? True Online $TD(\lambda)$ is a machine learning algorithm that seeks to approximate the ideal online $\lambda$-return algorithm.

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