Query: hash deep learning models

title: Compact Hash Code Learning With Binary Deep Neural Network
abstract: Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end to end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.

title: HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN
abstract: Deep learning to hash improves image retrieval performance by end to end representation learning and hash coding from training data with pairwise similarity information. Subject to the scarcity of similarity information that is often expensive to collect for many application domains, existing deep learning to hash methods may overfit the training data and result in substantial loss of retrieval quality. This paper presents HashGAN, a novel architecture for deep learning to hash, which learns compact binary hash codes from both real images and diverse images synthesized by generative models. The main idea is to augment the training data with nearly real images synthesized from a new Pair Conditional Wasserstein GAN (PC WGAN) conditioned on the pairwise similarity information. Extensive experiments demonstrate that HashGAN can generate high quality binary hash codes and yield state of the art image retrieval performance on three benchmarks, NUS WIDE, CIFAR 10, and MS COCO.

title: Learning to Hash with Graph Neural Networks for Recommender Systems
abstract: Recommender systems in industry generally include two stages: recall and ranking. Recall refers to efficiently identify hundreds of candidate items that user may interest in from a large volume of item corpus, while the latter aims to output a precise ranking list using complex ranking models. Recently, graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes. Specifically, a deep hashing with GNNs (HashGNN) is presented, which consists of two components, a GNN encoder for learning node representations, and a hash layer for encoding representations to hash codes. The whole architecture is trained end to end by jointly optimizing two losses, i.e. reconstruction loss from reconstructing observed links, and ranking loss from preserving the relative ordering of hash codes. A novel discrete optimization strategy based on straight through estimator (STE) with guidance is proposed. The principal idea is to avoid gradient magnification in back propagation of STE with continuous embedding guidance, in which we begin from learning an easier network that mimic the continuous embedding and let it evolve during the training until it finally goes back to STE. Comprehensive experiments over three publicly available and one real world Alibaba company datasets demonstrate that our model not only can achieve comparable performance compared with its continuous counterpart but also runs multiple times faster during inference.

title: Learning to Hash with Binary Deep Neural Network
abstract: This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing non smooth objective functions due to binarization. Moreover, we incorporate independence and balance properties in the direct and strict forms in the learning. Furthermore, we include similarity preserving property in our objective function. Our resulting optimization with these binary, independence, and balance constraints is difficult to solve. We propose to attack it with alternating optimization and careful relaxation. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.

title: Deep Hash Embedding for Large Vocab Categorical Feature Representations
abstract: Embedding learning for large vocabulary categorical features (e.g. user/item IDs, and words) is crucial for deep learning, and especially neural models for recommendation systems and natural language understanding tasks. Typically, the model creates a huge embedding table that each row represents a dedicated embedding vector for every feature value. In practice, to handle new (i.e. out of vocab) feature values and reduce the storage cost, the hashing trick is often adopted, that randomly maps feature values to a smaller number of hashing buckets. Essentially, thess embedding methods can be viewed as 1 layer wide neural networks with one hot encodings. In this paper, we propose an alternative embedding framework Deep Hash Embedding (DHE) with non one hot encodings and a deep neural network (embedding network) to compute embeddings on the fly without having to store them. DHE first encodes the feature value to a dense vector with multiple hashing functions and then applies a DNN to generate the embedding. DHE is collision free as the dense hashing encodings are unique identifiers for both in vocab and out of vocab feature values. The encoding module is deterministic, non learnable, and free of storage, while the embedding network is updated during the training time to memorize embedding information. Empirical results show that DHE outperforms state of the art hashing based embedding learning algorithms, and achieves comparable AUC against the standard one hot encoding, with significantly smaller model sizes. Our work sheds light on design of DNN based alternative embedding schemes for categorical features without using embedding table lookup.

title: Coin.AI: A Proof of Useful Work Scheme for Blockchain Based Distributed Deep Learning
abstract: One decade ago, Bitcoin was introduced, becoming the first cryptocurrency and establishing the concept of "blockchain" as a distributed ledger. As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies. However, many of them share one common feature: they require proof of work to support the generation of blocks (mining) and, eventually, the generation of money. This proof of work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute force. The main drawback of proof of work is that it requires ridiculously large amounts of energy which do not have any useful outcome beyond supporting the currency. In this paper, we present a theoretical proposal that introduces a proof of useful work scheme to support a cryptocurrency running over a blockchain, which we named Coin.AI. In this system, the mining scheme requires training deep learning models, and a block is only mined when the performance of such model exceeds a threshold. The distributed system allows for nodes to verify the models delivered by miners in an easy way (certainly much more efficiently than the mining process itself) determining when a block is to be generated. Additionally, this paper presents a proof of storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.

title: Privacy Preserving Collaborative Deep Learning Using Verifiable Multi Secret Sharing Scheme
abstract: Collaborative deep learning is an approach used to handle the amount of training data needed to build a better deep learning model. In collaborative deep learning, the central server collects user data and run the deep learning algorithm to get more accurate models. However, centralized training data collection can cause serious like privacy leakage problems and damage to the integrity of training data. In this paper, we introduce the privacy preserving collaborative deep learning model using verifiable (k, t, n) multi secret sharing based on the Elliptic Curve Diffie Hellman and SHA3 256 as a hash function. Where all training data will be formed into n shares using a session key generated from the private key and public key Elliptic Curve Diffie Hellman to protect the privacy and avoid all training data using SHA3 256 for the verification process before sending to the server. The test results show the integrity of damaged training data and colluding participants can be verified. In addition, the accuracy of the model produced using or without using Verifiable Multi Secret Sharing Scheme has the same value. Therefore proposed model can protect the privacy and integrity of training data and maintain the accuracy of the deep learning model.

title: Asymmetric Deep Hashing for Efficient Hash Code Compression
abstract: Benefiting from recent advances in deep learning, deep hashing methods have achieved promising performance in large scale image retrieval. To improve storage and computational efficiency, existing hash codes need to be compressed accordingly. However, previous deep hashing methods have to retrain their models and then regenerate the whole database codes using the new models when code length changes, which is time consuming especially for large image databases. In this paper, we propose a novel deep hashing method, called Code Compression oriented Deep Hashing (CCDH) for efficiently compressing hash codes. CCDH learns deep hash functions for query images, while learning a one hidden layer Variational Autoencoder (VAE) from existing hash codes. With such asymmetric design, CCDH can efficiently compress database codes only using the learned encoder of VAE. Furthermore, CCDH is flexible enough to be used with a variety of deep hashing methods. Extensive experiments on three widely used image retrieval benchmarks demonstrate that CCDH can significantly reduce the cost for compressing database codes when code length changes while keeping the state of the art retrieval accuracy.

title: Deep Like Hashing in Hash for Visual Retrieval: An Embarrassingly Simple Method
abstract: Existing hashing methods have yielded significant performance in image and multimedia retrieval, which can be categorized into two groups: shallow hashing and deep hashing. However, there still exist some intrinsic limitations among them. The former generally adopts a one step strategy to learn the hashing codes for discovering the discriminative binary feature, but the latent discriminative information in the learned hashing codes is not well exploited. The latter, as deep neural network based hashing models, can learn highly discriminative and compact features, but relies on large scale data and computation resources for numerous network parameters tuning with back propagation optimization. Straightforward training of deep hashing models from scratch on small scale data is almost impossible. Therefore, in order to develop efficient but effective learning to hash algorithm that depends only on small scale data, we propose a novel non neural network based deep like learning framework, i.e. multi level cascaded hashing (MCH) approach with hierarchical learning strategy, for image retrieval. The contributions are threefold. First, a hashing in hash architecture is designed in MCH, which inherits the excellent traits of traditional neural networks based deep learning, such that discriminative binary features that are beneficial to image retrieval can be effectively captured. Second, in each level the binary features of all preceding levels and the visual appearance feature are simultaneously cascaded as inputs of all subsequent levels to retrain, which fully exploits the implicated discriminative information. Third, a basic learning to hash (BLH) model with label constraint is proposed for hierarchical learning. Without loss of generality, the existing hashing models can be easily integrated into our MCH framework. We show experimentally on small and large scale visual retrieval tasks that our method outperforms several state of the arts.

title: Supervised Deep Learning for Hierarchical Image Data Retrieval
abstract: The techniques of feature extraction and representation on image data have been significantly progressed in recent years due to the development of deep learning. With a large number of representative image features being extracted from ImageNet by convolutional neural networks, many object recognizing applications were successfully accomplished effectively. In this paper, two supervised image retrieval models for retrieving images with similar hierarchical concept are investigated and compared. First, image features are extracted by pre trained VGG convolutional networks. Then, the supervised retrieval models are learned from a set of images with hierarchical concept labels. The experimental results show that the hash based model generally is superior to classifier based model both in F1 measure and MAP no matter what in coarse level or fine level of concept hierarchy.