Deep Supervised Hashing using Symmetric Relative Entropy

By virtue of their simplicity and efficiency, hashing algorithms have achieved significant success on large-scale approximate nearest neighbor search. Recently, many deep neural network based hashing methods have been proposed to improve the search accuracy by simultaneously learning both the feature representation and the binary hash functions. Most deep hashing methods depend on supervised semantic label information for preserving the distance or similarity between local structures, which unfortunately ignores the global distribution of the learned hash codes. We propose a novel deep supervised hashing method that aims to minimize the information loss generated during the embedding process. Specifically, the information loss is measured by the Jensen-Shannon divergence to ensure that compact hash codes have a similar distribution with those from the original images. Experimental results show that our method outperforms current state-of-the-art approaches on two benchmark datasets

Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

Aligning Language Models with Preferences through f-divergence Minimization

Aligning language models with preferences can be posed as approximating a target distribution representing some desired behavior. Existing approaches differ both in the functional form of the target distribution and the algorithm used to approximate it. For instance, Reinforcement Learning from Human Feedback (RLHF) corresponds to minimizing a reverse KL from an implicit target distribution arising from a KL penalty in the objective. On the other hand, Generative Distributional Control (GDC) has an explicit target distribution and minimizes a forward KL from it using the Distributional Policy Gradient (DPG) algorithm. In this paper, we propose a new approach, f-DPG, which allows the use of any f-divergence to approximate any target distribution. f-DPG unifies both frameworks (RLHF, GDC) and the approximation methods (DPG, RL with KL penalties). We show the practical benefits of various choices of divergence objectives and demonstrate that there is no universally optimal objective but that different divergences are good for approximating different targets. For instance, we discover that for GDC, the Jensen-Shannon divergence frequently outperforms forward KL divergence by a wide margin, leading to significant improvements over prior work

Efficient Learning Framework for Training Deep Learning Models with Limited Supervision

In recent years, deep learning has shown tremendous success in different applications, however these modes mostly need a large labeled dataset for training their parameters. In this work, we aim to explore the potentials of efficient learning frameworks for training deep models on different problems in the case of limited supervision or noisy labels. For the image clustering problem, we introduce a new deep convolutional autoencoder with an unsupervised learning framework. We employ a relative entropy minimization as the clustering objective regularized by the frequency of cluster assignments and a reconstruction loss. In the case of noisy labels obtained by crowdsourcing platforms, we proposed a novel deep hybrid model for sentiment analysis of text data like tweets based on noisy crowd labels. The proposed model consists of a crowdsourcing aggregation model and a deep text autoencoder. We combine these sub-models based on a probabilistic framework rather than a heuristic way, and derive an efficient optimization algorithm to jointly solve the corresponding problem. In order to improve the performance of unsupervised deep hash functions on image similarity search in big datasets, we adopt generative adversarial networks to propose a new deep image retrieval model, where the adversarial loss is employed as a data-dependent regularization in our objective function. We also introduce a balanced self-paced learning algorithm for training a GAN-based model for image clustering, where the input samples are gradually included into training from easy to difficult, while the diversity of selected samples from all clusters are also considered. In addition, we explore adopting discriminative approaches for unsupervised visual representation learning rather than the generative algorithms, such as maximizing the mutual information between an input image and its representation and a contrastive loss for decreasing the distance between the representations of original and augmented image data

공동 대조적 학습을 이용한 비지도 도메인 적응 기법 연구

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. 윤성로.Domain adaptation is introduced to exploit the label information of source domain when labels are not available for target domain. Previous methods minimized domain discrepancy in a latent space to enable transfer learning. These studies are based on the theoretical analysis that the target error is upper bounded by the sum of source error, the domain discrepancy, and the joint error of the ideal hypothesis. However, feature discriminability is sacrificed while enhancing the feature transferability by matching marginal distributions. In particular, the ideal joint hypothesis error in the target error upper bound, which was previously considered to be minute, has been found to be significant, impairing its theoretical guarantee. In this paper, to manage the joint error, we propose an alternative upper bound on the target error that explicitly considers it. Based on the theoretical analysis, we suggest a joint optimization framework that combines the source and target domains. To minimize the joint error, we further introduce Joint Contrastive Learning (JCL) that finds class-level discriminative features. With a solid theoretical framework, JCL employs contrastive loss to maximize the mutual information between a feature and its label, which is equivalent to maximizing the Jensen-Shannon divergence between conditional distributions. Extensive experiments on domain adaptation datasets demonstrate that JCL outperforms existing state-of-the-art methods.도메인 적응 기법은 타겟 도메인의 라벨 정보가 없는 상황에서 비슷한 도메인인 소스 도메인의 라벨 정보를 활용하기 위해 개발되었다. 기존의 방법론들은 잠재 공간에서 도메인들 사이의 분포 차이를 줄임으로써 전이 학습이 가능하게 하였다. 이러한 기법들은 소스 도메인의 에러율, 도메인 간 분포 차이, 그리고 양 도메인에서 이상적인 분류기의 에러율의 합이 타겟 도메인의 에러율의 상계가 된다는 이론을 바탕으로 한다. 그러나 도메인들 사이의 분포 차이를 줄이는 방법들은 동시에 잠재 공간에서 서로 다른 라벨을 갖는 데이터들 사이의 구별성을 감소시켰다. 특히, 작을 것이라 생각되던 양 도메인에서 이상적인 분류기의 에러율이 큰 것으로 나타났다. 본 논문에서는 기존의 이론에서는 다루지 않은 양 도메인에서 분류기의 에러율을 조절할 수 있게하기 위해 새로운 이론을 제시한다. 이 이론적 배경을 바탕으로 소스 도메인과 타겟 도메인을 함께 학습하는 공동 대조적 방법을 소개한다. 본 공동 대조적 학습 방법에서는 각 라벨별로 구분되는 잠재 공간을 학습하기 위해 각 데이터의 특징과 라벨 사이의 상호 정보량을 최대화한다. 이 각 데이터의 특징과 라벨 사이의 상호 정보량은 각 라벨 분포 사이의 젠센-샤논 거리와 같으므로 이를 최대화하는 것은 곧 라벨들이 잘 구별되는 잠재 공간을 학습하는 것이다. 마지막으로 공동 대조적 학습 방법을 여러 데이터 셋에 적용하여 기존 방법론들과 비교하였다.1 Introduction 1 2 Background 4 2.1 Domain Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Problem Setting and Notations . . . . . . . . . . . . . . . . . 4 2.1.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . 5 2.2 Approaches for Domain Adaptation . . . . . . . . . . . . . . . . . . 6 2.2.1 Marginal Distribution Alignment Based Approaches . . . . . 6 2.2.2 Conditional Distribution Matching Approaches . . . . . . . . 7 2.3 Contrastive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3 Method 10 3.1 An Alternative Upper Bound . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Joint Contrastive Learning . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.1 Theoretical Guarantees . . . . . . . . . . . . . . . . . . . . . 14 3.2.2 Generalization to Multiclass Classification . . . . . . . . . . 17 3.2.3 Training Procedure . . . . . . . . . . . . . . . . . . . . . . . 19 4 Experiments 24 4.1 Datasets and Baselines . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4 Ablation Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5 Conclusion 35 Abstract (In Korean) 45Maste