A.1 ProofofTheorem3.1 The proof can be found in (Hardt et al., 2016). We provide the proof in Appendix for reference. Firstly, it is easy to see that h1(w) and h2(w) are both η-approximate β-gradient Lipschitz, which satisfiesinequatilyinEq. (A.1). A.5 ProofofTheorem5.1 The proof follows the standard techniques for uniform stability. We need to replace the nonexpansive property used in standard analysis by the approximately non-expansive property.

Deep learning segmentation relies heavily on labeled data, but manual labeling is laborious and time-consuming, especially for volumetric images such as brain magnetic resonance imaging (MRI). While recent domain-randomization techniques alleviate the dependency on labeled data by synthesizing diverse training images from label maps, they offer limited anatomical variability when very few label maps are available. Semi-supervised self-training addresses label scarcity by iteratively incorporating model predictions into the training set, enabling networks to learn from unlabeled data. In this work, we combine domain randomization with self-training to train three-dimensional skull-stripping networks using as little as a single labeled example. First, we automatically bin voxel intensities, yielding labels we use to synthesize images for training an initial skull-stripping model. Second, we train a convolutional autoencoder (AE) on the labeled example and use its reconstruction error to assess the quality of brain masks predicted for unlabeled data. Third, we select the top-ranking pseudo-labels to fine-tune the network, achieving skull-stripping performance on out-of-distribution data that approaches models trained with more labeled images. We compare AE-based ranking to consistency-based ranking under test-time augmentation, finding that the AE approach yields a stronger correlation with segmentation accuracy. Our results highlight the potential of combining domain randomization and AE-based quality control to enable effective semi-supervised segmentation from extremely limited labeled data. This strategy may ease the labeling burden that slows progress in studies involving new anatomical structures or emerging imaging techniques.

This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so the robot can transfer this style to a scene with visual details that are significantly different from the input video. The framework builds upon CineMPC, a tool that allows users to control cinematographic features, like subjects' position on the image and the depth of field, by manipulating the intrinsics and extrinsics of a cinematographic camera. However, CineMPC requires a human expert to specify the desired style of the shot (composition, camera motion, zoom, focus, etc). CineTransfer bridges this gap, aiming a fully autonomous cinematographic platform. The user chooses a single input video as a style guide. CineTransfer extracts and optimizes two important style features, the composition of the subject in the image and the scene depth of field, and provides instructions for CineMPC to control the robot to record an output sequence that matches these features as closely as possible. In contrast with other style transfer methods, our approach is a lightweight and portable framework which does not require deep network training or extensive datasets. Experiments with real and simulated videos demonstrate the system's ability to analyze and transfer style between recordings, and are available in the supplementary video.

We describe a framework for learning an object classifier from a single example. This goal is achieved by emphasizing the relevant dimensions for classification using available examples of related classes. Learning to accurately classify objects from a single training example is often un- feasible due to overfitting effects. However, if the instance representa- tion provides that the distance between each two instances of the same class is smaller than the distance between any two instances from dif- ferent classes, then a nearest neighbor classifier could achieve perfect performance with a single training example. We therefore suggest a two stage strategy.

The naive approach: Take the derivative of the loss function which is an average of the losses calculated on every example in the dataset, a full update is powerful but it has some drawbacks… Drawbacks: . Can be extremely slow as we need to pass over the entire dataset to make a single update. . If there is a lot of redundancy in the training data, the benefit of a full update is very low The extreme approach Consider only a single example at a time and update steps based on one observation at a time, does that remind you of something?? Yes, it's the stochastic gradient descent algorithm or SGD. It can be effective even in large datasets but it also has some drawbacks… Drawbacks: . It can take longer to process one sample at a time compared to a full batch .

Batch Gradient Descent, Mini-batch Gradient Descent and Stochastic Gradient Descent are techniques used for gradient optimization differ in the batch size they use for computing gradients in each iteration. Gradient Descent uses all the data to compute gradients and update weights in each iteration. Minibatch Gradient Descent takes a subset of dataset to update its weights in each iteration. It however takes more iterations to converge to minima, but it is faster as compared to Gradient Descent due to lesser size of batch data used. Stochastic Gradient Descent (SGD) (or also sometimes on-line gradient descent) is the extreme case of this.

For a neural network to learn features from images in order to classify them we need data, lots of data. It is difficult for a model to learn from very few samples per class. MNIST dataset has nearly 60000 training images for numbers 0–9 (10 classes). We will implement One shot learning to build a model which will correctly make predictions given only a single example of each new class. As humans, when we are presented with new object, we quickly pickup patterns, shape and other features.

When comparing human with artificial intelligence, one major difference is apparent: Humans can generalize very broadly from sparse data sets because they are able to recombine and reintegrate data components in compositional manners. To investigate differences in efficient learning, Joshua B. Tenenbaum and colleagues developed the character challenge: First an algorithm is trained in generating handwritten characters. In a next step, one version of a new type of character is presented. An efficient learning algorithm is expected to be able to re-generate this new character, to identify similar versions of this character, to generate new variants of it, and to create completely new character types. In the past, the character challenge was only met by complex algorithms that were provided with stochastic primitives. Here, we tackle the challenge without providing primitives. We apply a minimal recurrent neural network (RNN) model with one feedforward layer and one LSTM layer and train it to generate sequential handwritten character trajectories from one-hot encoded inputs. To manage the re-generation of untrained characters, when presented with only one example of them, we introduce a one-shot inference mechanism: the gradient signal is backpropagated to the feedforward layer weights only, leaving the LSTM layer untouched. We show that our model is able to meet the character challenge by recombining previously learned dynamic substructures, which are visible in the hidden LSTM states. Making use of the compositional abilities of RNNs in this way might be an important step towards bridging the gap between human and artificial intelligence.

As someone who needs to run a business, big or small, you are inundated with articles and talks at conferences about how great AI is. You hear a lot about what it can do, about the outcomes of some sexy new research, and about vague assertions of how it will transform your business. In truth, it can and will transform your business, but only if you can overcome the barriers to entry. There is a lot of focus on the artificial intelligence itself; the machine learning model and its algorithms, its accuracy, and all the amazing new breakthroughs. This is all well and good, and definitely worth paying attention to.

WASHINGTON – For artificial intelligence and smart machines to really take off, computers are going to have to be able to think more like people, according to experts in the field who are making important progress toward that goal. Scientists on Thursday said they had created a computer model, or algorithm, that captures the unique human ability to grasp new concepts from a single example in a study involving learning unfamiliar handwritten alphabet characters. This and similar research has the twin goals of better understanding human learning and developing new, more human-like learning algorithms, New York University cognitive and data scientist Brenden Lake said. "We aimed to reverse-engineer how people learn about these simple visual concepts, in terms of identifying the types of computations that the mind may be performing, and testing these assumptions by trying to recreate the behavior," Lake said. The algorithm was designed to make a computer able to learn quickly from a single example in the way people do.