image classification Concepts

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy · 15 Jan 2025 · 209pp · 63,332 words

known as adversarial perturbations. Computer scientists are often worried about the robustness of their systems, and about vulnerabilities to adversarial attacks. In the context of image classification, for example, deep learning algorithms might be very successful at identifying different kinds of animals in photos. In the test and training data, they might

The Alignment Problem: Machine Learning and Human Values

by Brian Christian · 5 Oct 2020 · 625pp · 167,349 words

of aesthetic possibility but have important diagnostic uses as well. For example, Mordvintsev, Olah, and Tyka used their start-from-static technique to have an image classification system “generate” images that would maximally resemble all of its different categories. “In some cases,” they write, “this reveals that the neural net isn’t

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was needed to render an Atari screen intelligible. He recalls, “The group said, Hey, we have these convolutional networks. They’ve been phenomenal at doing image classification. Um, what if we replace your feature-construction mechanism, which is still a bit of a kludge, by just a convolutional neural network?” Bellemare, again

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techniques and insights. See Simonyan and Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”; Howard, “Some Improvements on Deep Convolutional Neural Network Based Image Classification”; and Simonyan, Vedaldi, and Zisserman, “Deep Inside Convolutional Networks.” In 2018 and 2019, there was some internal controversy within Clarifai over whether its image-recognition

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. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, 1109–17, 2016. Howard, Andrew G. “Some Improvements on Deep Convolutional Neural Network Based Image Classification.” arXiv Preprint arXiv:1312.5402, 2013. Howard, John W., and Robyn M. Dawes. “Linear Prediction of Marital Happiness.” Personality and Social Psychology Bulletin 2, no

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://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/. Simonyan, Karen, Andrea Vedaldi, and Andrew Zisserman. “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.” arXiv Preprint arXiv:1312.6034, 2013. Simonyan, Karen, and Andrew Zisserman. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words

objects is the subject of the next section, and actually detecting the objects in images is the subject of Section 27.5. 27.4Classifying Images Image classification applies to two main cases. In one, the images are of objects, taken from a given taxonomy of classes, and there’s not much else

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a couch and lamp, but you don’t expect a giraffe or a submarine in a living room. We now have methods for large-scale image classification that can accurately output “grassland” or “living room.” Modern systems classify images using appearance (i.e., color and texture, as opposed to geometry). There are

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systems, much better than anyone has been able to produce with other methods. The ImageNet data set played a historic role in the development of image classification systems by providing them with over 14 million training images, classified into over 30,000 fine-grained categories. ImageNet also spurred progress with an annual

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by CNN classifiers are learned from data, not hand-crafted by a researcher; this ensures that the features are actually useful for classification. Progress in image classification has been rapid because of the availability of large, challenging data sets such as ImageNet; because of competitions based on these data sets that are

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, making it easy for others to fiddle with successful architectures and try to make them better. 27.4.2Why convolutional neural networks classify images well Image classification is best understood by looking at data sets, but ImageNet is much too large to look at in detail. The MNIST data set is a

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with words attached, it is natural to try and build tagging systems that tag images with relevant words. The underlying machinery is straightforward—we apply image classification and object detection methods and tag the image with the output words. But tags aren’t a comprehensive description of what is happening in an

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detection data set, PASCAL VOC, argued in favor of hand-designed features. This changed when Krizhevsky et al. (2013) showed that on the task of image classification on the ImageNet data set, their neural network (called AlexNet) gave significantly lower error rates than the mainstream computer vision techniques. What was the secret

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(Harrow et al., 2009; Dervovic et al., 2018), but no quantum computer capable of running them. We have some example applications of tasks such as image classification (Mott et al., 2017) where quantum algorithms are as good as classical algorithms on small problems. Current quantum computers handle only a few tens of

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sensing. Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2, 1–25. Ying, C., Kumar, S., Chen, D., Wang, T., and Cheng, Y. (2018). Image classification at supercomputer scale. arXiv:1811.06992. Yip, K. M.‑K. (1991). KAM: A System for Intelligently Guiding Numerical Experimentation by Computer. MIT Press. Yngve, V

The Elements of Statistical Learning (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani and Jerome Friedman · 25 Aug 2009 · 764pp · 261,694 words

which facilitates construction of nonlinear boundaries in a manner very similar to the support vector machines, penalized discriminant analysis for problems such as signal and image classification where the large number of features are highly correlated, and mixture discriminant analysis for irregularly shaped classes. 12.2 The Support Vector Classifier In Chapter

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six are near zero. Operationally, we project the data into the leading four-dimensional subspace, and then carry out nearest neighbor classification. In the satellite image classification example in Section 13.3.2, the technique labeled DANN in Figure 13.8 used 5-nearest-neighbors in a globally reduced subspace. There are

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to avoid overfitting was introduced by Kleinberg (1990), and later in Kleinberg (1996). Amit and Geman (1997) used randomized trees grown on image features for image classification problems. Breiman (1996a) introduced bagging, a precursor to his version of random forests. Dietterich (2000b) also proposed an improvement on bagging using additional randomization. His

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron · 13 Mar 2017 · 1,331pp · 163,200 words

binary class (e.g., spam/ham, urgent/not-urgent, and so on). When the classes are exclusive (e.g., classes 0 through 9 for digit image classification), the output layer is typically modified by replacing the individual activation functions by a shared softmax function (see Figure 10-9). The softmax function was

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complex problems, you can gradually ramp up the number of hidden layers, until you start overfitting the training set. Very complex tasks, such as large image classification or speech recognition, typically require networks with dozens of layers (or even hundreds, but not fully connected ones, as we will see in Chapter 13

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were able to use much larger learning rates, significantly speeding up the learning process. Specifically, they note that “Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. […] Using an ensemble

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is likely that the lower layers of the first DNN have learned to detect low-level features in pictures that will be useful across both image classification tasks, so you can just reuse these layers as they are. It is generally a good idea to “freeze” their weights when training the new

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public. TensorFlow has its own model zoo available at https://github.com/tensorflow/models. In particular, it contains most of the state-of-the-art image classification nets such as VGG, Inception, and ResNet (see Chapter 13, and check out the models/slim directory), including the code, the pretrained models, and tools

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measure of this progress is the error rate in competitions such as the ILSVRC ImageNet challenge. In this competition the top-5 error rate for image classification fell from over 26% to barely over 3% in just five years. The top-five error rate is the number of test images for which

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it possible to apply filters to arbitrary sets of inputs channels. Exercises What are the advantages of a CNN over a fully connected DNN for image classification? Consider a CNN composed of three convolutional layers, each with 3 × 3 kernels, a stride of 2, and SAME padding. The lowest layer outputs 100

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with the estimated probability (the list of class names is available at https://goo.gl/brXRtZ). How accurate is the model? Transfer learning for large image classification. Create a training set containing at least 100 images per class. For example, you could classify your own pictures based on the location (beach, mountain

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all time steps. Training a Sequence Classifier Let’s train an RNN to classify MNIST images. A convolutional neural network would be better suited for image classification (see Chapter 13), but this makes for a simple example that you are already familiar with. We will treat each image as a sequence of

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other examples of tasks where Reinforcement Learning is well suited, such as self-driving cars, placing ads on a web page, or controlling where an image classification system should focus its attention. Policy Search The algorithm used by the software agent to determine its actions is called its policy. For example, the

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https://github.com/ageron/handson-ml. Chapter 13: Convolutional Neural Networks These are the main advantages of a CNN over a fully connected DNN for image classification: Because consecutive layers are only partially connected and because it heavily reuses its weights, a CNN has many fewer parameters than a fully connected DNN

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Learning hypothesis boosting (see boosting) hypothesis function, Linear Regression hypothesis, null, Regularization Hyperparameters I identity matrix, Ridge Regression, Quadratic Programming ILSVRC ImageNet challenge, CNN Architectures image classification, CNN Architectures impurity measures, Making Predictions, Gini Impurity or Entropy? in-graph replication, In-Graph Versus Between-Graph Replication inception modules, GoogLeNet Inception-v4, ResNet

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt · 8 Oct 2013 · 523pp · 112,185 words

. Oh wait, 1992. Whatever. Beautiful Soup Robust but kind of slow. Mechanize (or here) Super cool as well, but it doesn’t parse JavaScript. PostScript Image classification. Thought Experiment: Image Recognition How do you determine if an image is a landscape or a headshot? Start with collecting data. You either need to

Succeeding With AI: How to Make AI Work for Your Business

by Veljko Krunic · 29 Mar 2020

’s say a radiology department of a large hospital. You’re lucky to have on the team the best AI expert in the field of image classification, who has you covered on the AI side. While you’re confident that expert will be able to develop an AI algorithm to classify medical

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’s say a radiology department of a large hospital. You’re lucky to have on the team the best AI expert in the field of image classification, who has you covered on the AI side. While you’re confident that expert will be able to develop an AI algorithm to classify medical

Mastering Machine Learning With Scikit-Learn

by Gavin Hackeling · 31 Oct 2014

Banerjee Adithi Shetty Cover Work Kyle Albuquerque www.it-ebooks.info About the Author Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's

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, a compression technique that represents a range of colors with a single color. We also used K-Means to learn features in a semi-supervised image classification problem. In the next chapter, we will discuss another unsupervised learning task called dimensionality reduction. Like the semi-supervised feature representations we created to classify

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson and Michael Munn · 31 Oct 2020

, for example) because we assume that you will be using a pre-built model architecture (such as ResNet-50 or GRUCell), not writing your own image classification or recurrent neural network. Here are some concrete examples of areas that we intentionally stay away from because we believe that these topics are more

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potential for improvements in performance by making different choices in these sorts of things tends to be minor. ML model architectures If you are doing image classification, we recommend that you use an off-the-shelf model like ResNet or whatever the latest hotness is at the time you are reading this

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. Leave the design of new image classification or text classification models to researchers who specialize in this problem. Model layers You won’t find convolutional neural networks or recurrent neural networks in

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worth searching within this range. Autoencoders Training embeddings in a supervised way can be hard because it requires a lot of labeled data. For an image classification model like Inception to be able to produce useful image embeddings, it is trained on ImageNet, which has 14 million labeled images. Autoencoders provide one

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unlabeled data we have to go from high cardinality to lower cardinality by using autoencoders as an auxiliary learning task. Then, we solve the actual image classification problem for which we typically have much less labeled data using the embedding produced by the auxiliary autoencoder task. This is likely to boost model

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or as a category, an image, or free-form text. Many off-the-shelf models are defined for specific types of input only—a standard image classification model such as Resnet-50, for example, does not have the ability to handle inputs other than images. To understand the need for multimodal inputs

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be assigned more than one label, which is what this pattern addresses. The Multilabel design pattern exists for models trained on all data modalities. For image classification, in the earlier cat, dog, rabbit example, we could instead use training images that each depicted multiple animals, and could therefore have multiple labels. For

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that belong to each overlapping combination of labels. When exploriing relationships between labels in our dataset, we may also encounter hierarchical labels. ImageNet, the popular image classification dataset, contains thousands of labeled images and is often used as a starting point for transfer learning on image models. All of the labels used

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scenario of using a pre-trained model as the first step of a pipeline is using an object-detection model followed by a fine-grained image classification model. For example, the object-detection model might find all handbags in the image, an intermediate step might crop the image to the bounding boxes

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satellite images. By similar task, we’re referring to the problem being solved. To do transfer learning for image classification, for example, it is better to start with a model that has been trained for image classification, rather than object detection. Continuing with the example, let’s say we’re building a binary classifier

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learning. To solve this with transfer learning, we’ll need to find a model that has already been trained on a large dataset to do image classification. We’ll then remove the last layer from that model, freeze the weights of that model, and continue training using our 400 x-ray images

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the datasets are different, so long as the prediction task is the same. In this case we’re doing image classification. You can use transfer learning for many prediction tasks in addition to image classification, so long as there is an existing pre-trained model that matches the task you’d like to perform

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. It has retained enough of the information from the input image to be able to classify it. When we apply this model to our medical image classification task, we hope that the information distillation will be sufficient to successfully carry out classification on our dataset. The histology dataset comes with images as

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be using our MobileNet model trained on ImageNet as a basis for doing transfer learning on a dataset of medical images. Although both tasks involve image classification, the nature of the images in each dataset are very different. Focus on image and text models You may have noticed that all of the

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in seconds or minutes, but it can quickly get expensive on larger models that require significant training time and infrastructure. Imagine you are training an image classification model that takes hours to train on GPUs. You settle on a few hyperparameter values to try and then wait for the results of the

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is a question we need to be ready to handle whenever we develop a model and present it to business stakeholders. If we train an image classification model on items in a product catalog and the mean average precision (MAP) is 95%, we can expect to be asked: “Is a MAP of

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a good MAE for the bicycle rental problem in New York City? How about in London? What is a good MAP for the product catalog image classification task? Model performance is typically stated in terms of cold, hard numbers that are difficult for end users to put into context. Explaining the formula

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extent to which human physicians make errors and compare the error rate of the model against that of human experts. In the case of such image classification problems, this is a natural extension of the labeling phase because the labels for eye disease are created through human labeling. It is sometimes advantageous

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this model, since deploying or changing a production model always carries a certain cost in terms of reliability and error budgets. For example, if the image classification model is used to pre-fill an order form, we can calculate that a 1% improvement will translate to 20 fewer abandoned orders per day

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type of explanation methods we’ll discuss are known as feature attributions. These methods aim to attribute a model’s output—whether it be an image, classification, or numerical value—to its features, by assigning attribution values to each feature indicating how much that feature contributed to the output. There are two

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, Draw! and example-based explanations, see this paper. 8 For a more detailed look on how race and gender bias can find their way into image classification models, see Joy Buolamwini and Timmit Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification”, Proceedings of Machine Learning Research 81 (2018): 1-15

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that might rely on human vision, from using an MRI to detect lung cancer to self-driving cars. Some classical applications of computer vision are image classification, video motion analysis, image segmentation, and image denoising. Reframing Neutral Class Multimodal Input Transfer Learning Embeddings Multilabel Cascade Two-Phase Predictions Predictive Analytics Predictive modeling

Artificial Intelligence: A Guide for Thinking Humans

by Melanie Mitchell · 14 Oct 2019 · 350pp · 98,077 words

network. The decoder network “decodes” these activations to output a sentence. To encode the image, the authors used a ConvNet that had been trained for image classification on ImageNet, the huge image data set that I described in chapter 5. The task here is to train the decoder network to generate an