Author name cluster

Jianchao Yang

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8 papers

2 author rows

ICLR Conference 2020 Conference Paper

AtomNAS: Fine-Grained End-to-End Neural Architecture Search

Jieru Mei
Yingwei Li 0002
Xiaochen Lian
Xiaojie Jin
Linjie Yang
Alan L. Yuille
Jianchao Yang

Search space design is very critical to neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that is much smaller than the ones used in recent NAS algorithms. This search space allows a mix of operations by composing different types of atomic blocks, while the search space in previous methods only allows homogeneous operations. Based on this search space, we propose a resource-aware architecture search framework which automatically assigns the computational resources (e.g., output channel numbers) for each operation by jointly considering the performance and the computational cost. In addition, to accelerate the search process, we propose a dynamic network shrinkage technique which prunes the atomic blocks with negligible influence on outputs on the fly. Instead of a search-and-retrain two-stage paradigm, our method simultaneously searches and trains the target architecture. Our method achieves state-of-the-art performance under several FLOPs configurations on ImageNet with a small searching cost. We open our entire codebase at: https://github.com/meijieru/AtomNAS.

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ICLR Conference 2020 Conference Paper

Neural Epitome Search for Architecture-Agnostic Network Compression

Daquan Zhou
Xiaojie Jin
Qibin Hou
Kaixin Wang
Jianchao Yang
Jiashi Feng

Traditional compression methods including network pruning, quantization, low rank factorization and knowledge distillation all assume that network architectures and parameters should be hardwired. In this work, we propose a new perspective on network compression, i.e., network parameters can be disentangled from the architectures. From this viewpoint, we present the Neural Epitome Search (NES), a new neural network compression approach that learns to find compact yet expressive epitomes for weight parameters of a specified network architecture end-to-end. The complete network to compress can be generated from the learned epitome via a novel transformation method that adaptively transforms the epitomes to match shapes of the given architecture. Compared with existing compression methods, NES allows the weight tensors to be independent of the architecture design and hence can achieve a good trade-off between model compression rate and performance given a specific model size constraint. Experiments demonstrate that, on ImageNet, when taking MobileNetV2 as backbone, our approach improves the full-model baseline by 1.47% in top-1 accuracy with 25% MAdd reduction and AutoML for Model Compression (AMC) by 2.5% with nearly the same compression ratio. Moreover, taking EfficientNet-B0 as baseline, our NES yields an improvement of 1.2% but are with 10% less MAdd. In particular, our method achieves a new state-of-the-art results of 77.5% under mobile settings (<350M MAdd). Code will be made publicly available.

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ICML Conference 2018 Conference Paper

WSNet: Compact and Efficient Networks Through Weight Sampling

Xiaojie Jin
Yingzhen Yang
Ning Xu 0001
Jianchao Yang
Nebojsa Jojic
Jiashi Feng
Shuicheng Yan

We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via ad hoc processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces parameter sharing throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to 180x smaller and theoretically up to 16x faster than the well-established baselines, without noticeable performance drop.

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UAI Conference 2017 Conference Paper

Neighborhood Regularized l^1-Graph

Yingzhen Yang
Jiashi Feng
Jiahui Yu
Jianchao Yang
Thomas S. Huang

`1 -Graph, which learns a sparse graph over the data by sparse representation, has been demonstrated to be effective in clustering especially for high dimensional data. Although it achieves compelling performance, the sparse graph generated by `1 -Graph ignores the geometric information of the data by sparse representation for each datum separately. To obtain a sparse graph that is aligned to the underlying manifold structure of the data, we propose the novel Neighborhood Regularized `1 -Graph (NR`1 -Graph). NR`1 -Graph learns sparse graph with locally consistent neighborhood by encouraging nearby data to have similar neighbors in the constructed sparse graph. We present the optimization algorithm of NR`1 -Graph with theoretical guarantee on the convergence and the gap between the suboptimal solution and the globally optimal solution in each step of the coordinate descent, which is essential for the overall optimization of NR`1 -Graph. Its provable accelerated version, NR`1 -Graph by Random Projection (NR`1 -Graph-RP) that employs randomized data matrix decomposition, is also presented to improve the efficiency of the optimization of NR`1 -Graph. Experimental results on various real data sets demonstrate the effectiveness of both NR`1 -Graph and NR`1 Graph-RP. This work is supported in part by US Army Research Office grant W911NF-15-1-0317. The work of Jiashi Feng was supported by NUS startup R-263-000-C08-133, MOE R-263000-C21-112 and IDS R-263-000-C67-646. Pushmeet Kohli was at Microsoft Research during this project.

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AAAI Conference 2016 Conference Paper

Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark

Quanzeng You
Jiebo Luo
Hailin Jin
Jianchao Yang

Psychological research results have conﬁrmed that people can have different emotional reactions to different visual stimuli. Several papers have been published on the problem of visual emotion analysis. In particular, attempts have been made to analyze and predict people’s emotional reaction towards images. To this end, different kinds of hand-tuned features are proposed. The results reported on several carefully selected and labeled small image data sets have conﬁrmed the promise of such features. While the recent successes of many computer vision related tasks are due to the adoption of Convolutional Neural Networks (CNNs), visual emotion analysis has not achieved the same level of success. This may be primarily due to the unavailability of conﬁdently labeled and relatively large image data sets for visual emotion analysis. In this work, we introduce a new data set, which started from 3+ million weakly labeled images of different emotions and ended up 30 times as large as the current largest publicly available visual emotion data set. We hope that this data set encourages further research on visual emotion analysis. We also perform extensive benchmarking analyses on this large data set using the state of the art methods including CNNs.

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AAAI Conference 2015 Conference Paper

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Quanzeng You
Jiebo Luo
Hailin Jin
Jianchao Yang

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We ﬁrst design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to ﬁne-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.

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AAAI Conference 2014 Conference Paper

Data Clustering by Laplacian Regularized L1-Graph

Yingzhen Yang
Zhangyang Wang
Jianchao Yang
Jiangping Wang
Shiyu Chang
Thomas Huang

`1 -Graph has been proven to be effective in data clustering, which partitions the data space by using the sparse representation of the data as the similarity measure. However, the sparse representation is performed for each datum separately without taking into account the geometric structure of the data. Motivated by `1 -Graph and manifold leaning, we propose Laplacian Regularized `1 -Graph (LR`1 -Graph) for data clustering. The sparse representations of LR`1 -Graph are regularized by the geometric information of the data so that they vary smoothly along the geodesics of the data manifold by the graph Laplacian according to the manifold assumption. Moreover, we propose an iterative regularization scheme, where the sparse representation obtained from the previous iteration is used to build the graph Laplacian for the current iteration of regularization. The experimental results on real data sets demonstrate the superiority of our algorithm compared to `1 -Graph and other competing clustering methods.

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NeurIPS Conference 2014 Conference Paper

Scale Adaptive Blind Deblurring

Haichao Zhang
Jianchao Yang

The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure. We present a scale space perspective on blind deblurring algorithms, and introduce a cascaded scale space formulation for blind deblurring. This new formulation suggests a natural approach robust to noise and small scale structures through tying the estimation across multiple scales and balancing the contributions of different scales automatically by learning from data. The proposed formulation also allows to handle non-uniform blur with a straightforward extension. Experiments are conducted on both benchmark dataset and real-world images to validate the effectiveness of the proposed method. One surprising finding based on our approach is that blur kernel estimation is not necessarily best at the finest scale.

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