Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise and unary potentials. The above is naturally modeled with a graph, where edges and vertices are related to pairwise and unary potentials, respectively. We consider the generative process proposed by Globerson et al. and apply it to general connected graphs. We analyze the structural conditions of the graph that allow for the exact recovery of the labels. Our results show that exact recovery is possible and achievable in polynomial time for a large class of graphs. In particular, we show that graphs that are bad expanders can be exactly recovered by adding small edge perturbations coming from the Erd\H{o}s-R\'enyi model. Finally, as a byproduct of our analysis, we provide an extension of Cheeger's inequality.

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, while the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data for improving the learning model. Extensive experiments on both artificial and real-world data sets demonstrate that our method can achieve superior or comparable performance against the state-of-the-art methods.

In this paper, we propose a general framework for using adversarial label learning (ALL) [1] for multiclass classification when the data is weakly supervised. We introduce a new variant of ALL that incorporates human knowledge through multiple constraint types. Like adversarial label learning, we learn by adversarially finding labels constrained to be partially consistent with the weak supervision. However, we describe a different approach to solve the optimization that enjoys faster convergence when training large deep models. Our framework allows for human knowledge to be encoded into the algorithm as a set of linear constraints. We then solve a two-player game optimization subject to these constraints. We test our method on three data sets by training convolutional neural network models that learn to classify image objects with limited access to training labels. Our approach is able to learn even in settings where the weak supervision confounds state-of-the-art weakly supervised learning methods. The results of our experiments demonstrate the applicability of this approach to general classification tasks.

In this paper, we aim to recover the 3D human pose from 2D body joints of a single image. The major challenge in this task is the depth ambiguity since different 3D poses may produce similar 2D poses. Although many recent advances in this problem are found in both unsupervised and supervised learning approaches, the performances of most of these approaches are greatly affected by insufficient diversities and richness of training data. To alleviate this issue, we propose an unsupervised learning approach, which is capable of estimating various complex poses well under limited available training data. Specifically, we propose a Shape Decomposition Model (SDM) in which a 3D pose is considered as the superposition of two parts which are global structure together with some deformations. Based on SDM, we estimate these two parts explicitly by solving two sets of different distributed combination coefficients of geometric priors. In addition, to obtain geometric priors, a joint dictionary learning algorithm is proposed to extract both coarse and fine pose clues simultaneously from limited training data. Quantitative evaluations on several widely used datasets demonstrate that our approach yields better performances over other competitive approaches. Especially, on some categories with more complex deformations, significant improvements are achieved by our approach. Furthermore, qualitative experiments conducted on in-the-wild images also show the effectiveness of the proposed approach.

If you've ever wondered what a loaf of bread would look like as a cat, edges2cats is for you. The program that turns sketches into images of cats is one of many whimsical creations inspired by Phillip Isola's image-to-image translation software released in the early days of generative adversarial networks, or GANs. In a 2016 paper, Isola and his colleagues showed how a new type of GAN could transform a hand-drawn shoe into its fashion-photo equivalent, or turn an aerial photo into a grayscale map. Later, the researchers showed how landscape photos could be reimagined in the impressionist brushstrokes of Monet or Van Gogh. Now an assistant professor in MIT's Department of Electrical Engineering and Computer Science, Isola continues to explore what GANs can do.

Traditional structured prediction models try to learn the conditional likelihood, i.e., p(y x), to capture the relationship between the structured output y and the input features x. For many models, computing the likelihood is intractable. These models are therefore hard to train, requiring the use of surrogate objectives or variational inference to approximate likelihood. In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning. C-Glow benefits from the ability of flow-based models to compute p(y x) exactly and efficiently. Learning with c-Glow does not require a surrogate objective or performing inference during training. Once trained, we can directly and efficiently generate conditional samples to do structured prediction. We evaluate this approach on different structured prediction tasks and find c-Glow's structured outputs comparable in quality with state-of-the-art deep structured prediction approaches.

Hashing based visual search has attracted extensive research Online hashing has attracted extensive research attention attention in recent years due to the rapid growth of when facing streaming data. Most online hashing visual data on the Internet [7, 33, 8, 26, 12, 13, 30, 32, 25, methods, learning binary codes based on pairwise similarities 35, 27]. In various scenarios, online hashing has become of training instances, fail to capture the semantic relationship, a hot topic due to the emergence of handling the streaming and suffer from a poor generalization in largescale data, which aims to resolve an online retrieval task by applications due to large variations. In this paper, we updating the hash functions from sequentially arriving data propose to model the similarity distributions between the input instances. On one hand, online hashing takes advantages data and the hashing codes, upon which a novel supervised of traditional offline hashing methods, i.e., low storage cost online hashing method, dubbed as Similarity Distribution and efficiency of pairwise distance computation in the Hamming based Online Hashing (SDOH), is proposed, to keep space. On the other hand, it also merits in training the intrinsic semantic relationship in the produced Hamming efficiency and scalability for large-scale applications, since space. Specifically, we first transform the discrete the hash functions are updated instantly and solely based on similarity matrix into a probability matrix via a Gaussianbased the current streaming data, which is superior to traditional normalization to address the extremely imbalanced hashing methods based on a hashing model entirely trained distribution issue. And then, we introduce a scaling Student from scratch.

A machine learning model that generalizes well should obtain low errors on the unseen test examples. Test examples could be understood as perturbations of training examples, which means that if we know how to optimally perturb training examples to simulate test examples, we could achieve better generalization at test time. However, obtaining such perturbation is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a meta-learning framework that learns to perturb the latent features of training examples for generalization. Specifically, we meta-learn a noise generator that will output the optimal noise distribution for latent features across all network layers to obtain low error on the test instances, in an input-dependent manner. Then, the learned noise generator will perturb the training examples of unseen tasks at the meta-test time. We show that our method, Meta-dropout, could be also understood as meta-learning of the variational inference framework for a specific graphical model, and describe its connection to existing regularizers. Finally, we validate Meta-dropout on multiple benchmark datasets for few-shot classification, whose results show that it not only significantly improves the generalization performance of meta-learners but also allows them to obtain fast converegence.

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be released outside certain premises for privacy and/or security reasons, using GAN- synthetic data to conduct training is a plausible alternative. This paper proposes a generalization bound to guarantee the generalization capability of a classifier learning from GAN-synthetic data. This generalization bound helps developers gauge the generalization gap between learning from synthetic data and testing on real data, and can therefore provide the clues to improve the generalization capability.

Learning by demonstration is a versatile and rapid mechanism for transferring motor skills from a teacher to a learner. A particular challenge in imitation learning is the so-called correspondence problem, which involves mapping actions between a teacher and a learner having substantially different embodiments (say, human to robot). We present a general, model free and non-parametric imitation learning algorithm based on regression between two Hilbert spaces. We accomplish this via Kirszbraun's extension theorem --- apparently the first application of this technique to supervised learning --- and analyze its statistical and computational aspects. We begin by formulating the correspondence problem in terms of quadratically constrained quadratic program (QCQP) regression. Then we describe a procedure for smoothing the training data, which amounts to regularizing hypothesis complexity via its Lipschitz constant. The Lipschitz constant is tuned via a Structural Risk Minimization (SRM) procedure, based on the covering-number risk bounds we derive. We apply our technique to a static posture imitation task between two robotic manipulators with different embodiments, and report promising results.