DeepNLP/ICLR-2023-Accepted-Papers huggingface dataset:
S-NeRF: Neural Radiance Fields for Street Views
Authors: Ziyang Xie,Junge Zhang,Wenye Li,Feihu Zhang,Li Zhang

Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, "floaters" and other artifacts on  street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale background scenes and the foreground moving vehicles jointly. Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views. We also use the the noisy and sparse LiDAR points to boost the training and learn a robust geometry and reprojection based confidence to address the depth outliers.   Moreover, we extend our S-NeRF for reconstructing moving vehicles that is impracticable for conventional NeRFs. Thorough experiments on the large-scale driving datasets (e.g., nuScenes and Waymo) demonstrate that our method beats the state-of-the-art rivals by reducing 7～40% of the mean-squared error in the street-view synthesis and a 45% PSNR gain for the moving vehicles rendering.
Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization
Authors: Haiyang Yang,Xiaotong Li,SHIXIANG TANG,Feng Zhu,Yizhou Wang,Meilin Chen,LEI BAI,Rui Zhao,Wanli Ouyang

Self-supervised learning methods undergo undesirable performance drops when there exists a significant domain gap between training and testing scenarios. Therefore, unsupervised domain generalization (UDG) is proposed to tackle the problem, which requires the model to be trained on several different domains without supervision and generalize well on unseen test domains. Existing methods either rely on a cross-domain and semantically consistent image pair in contrastive methods or the reconstruction pair in generative methods, while the precious image pairs are not available without semantic labels. In this paper, we propose a cycle cross-domain reconstruction task for unsupervised domain generalization in the absence of paired images. The cycle cross-domain reconstruction task converts a masked image from one domain to another domain and then reconstructs the original image from the converted images. To preserve the divergent domain knowledge of decoders in the cycle reconstruction task, we propose a novel domain-contrastive loss to regularize the domain information in reconstructed images encoded with the desirable domain style. Qualitative results on extensive datasets illustrate our method improves the state-of-the-art unsupervised domain generalization methods by average $\textbf{+5.59\%}, \textbf{+4.52\%}, \textbf{+4.22\%}, \textbf{+7.02\%}$ on $1\%, 5\%, 10\%, 100\%$ PACS, and $\textbf{+5.08\%}, \textbf{+6.49\%}, \textbf{+1.79\%}, \textbf{+0.53\%}$ on $1\%, 5\%, 10\%, 100\%$ DomainNet, respectively.
CFlowNets: Continuous Control with Generative Flow Networks
Authors: Yinchuan Li,Shuang Luo,Haozhi Wang,Jianye HAO

Generative flow networks (GFlowNets), as an emerging technique, can be used as an alternative to reinforcement learning for exploratory control tasks. GFlowNets aims to sample actions with a probability proportional to the reward, similar to sampling different candidates in an active learning fashion. However, existing GFlowNets cannot adapt to continuous control tasks because GFlowNets need to form a DAG and compute the flow matching loss by traversing the inflows and outflows of each node in the trajectory. In this paper, we propose generative continuous flow networks (CFlowNets) that can be applied to continuous control tasks. First, we present the theoretical formulation of CFlowNets. Then, a training framework for CFlowNets is proposed, including the action selection process, the flow approximation algorithm, and the continuous flow matching loss function. Afterward, we theoretically prove the error bound of the flow approximation. The error decreases rapidly as the number of flow samples increases. Finally, experimental results on continuous control tasks demonstrate the performance advantages of CFlowNets compared to many reinforcement learning methods, especially regarding exploration ability.
Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
Authors: Dongzhuo Li

Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensional standard Gaussian distribution during inversion, particularly in the presence of data noise and inaccurate forward models, leading to low-fidelity solutions. To address this issue, we propose to reparameterize and Gaussianize the latent tensors using novel differentiable data-dependent layers wherein custom operators are defined by solving optimization problems. These proposed layers constrain inverse problems to obtain high-fidelity in-distribution solutions. We validate our technique on three inversion tasks: compressive-sensing MRI, image deblurring, and eikonal tomography (a nonlinear PDE-constrained inverse problem) using two representative deep generative models: StyleGAN2 and Glow. Our approach achieves state-of-the-art performance in terms of accuracy and consistency.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Authors: Jinrong Yang,Lin Song,Songtao Liu,Weixin Mao,Zeming Li,Xiaoping Li,Hongbin Sun,Jian Sun,Nanning Zheng

Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rules, making it difficult to handle complicated scenes. Different from them, we propose a Dynamic Ball Query (DBQ) network to adaptively select a subset of input points according to the input features, and assign the feature transform with a suitable receptive field for each selected point. It can be embedded into some state-of-the-art 3D detectors and trained in an end-to-end manner, which significantly reduces the computational cost. Extensive experiments demonstrate that our method can reduce latency by 30%-100% on KITTI, Waymo, and ONCE datasets. Specifically, the inference speed of our detector can reach 162 FPS on KITTI scene, and 30 FPS on Waymo and ONCE scenes without performance degradation. Due to skipping the redundant points, some evaluation metrics show significant improvements.
Exploring Low-Rank Property in Multiple Instance Learning for Whole Slide Image Classification
Authors: Jinxi Xiang,Jun Zhang

The classification of gigapixel-sized whole slide images (WSIs) with slide-level labels can be formulated as a multiple-instance-learning (MIL) problem. State-of-the-art models often consist of two decoupled parts: local feature embedding with a pre-trained model followed by a global feature aggregation network for classification. We leverage the properties of the apparent similarity in high-resolution WSIs, which essentially exhibit \textit{low-rank} structures in the data manifold, to develop a novel MIL with a boost in both feature embedding and feature aggregation. We extend the contrastive learning with a pathology-specific Low-Rank Constraint  (LRC) for feature embedding to pull together samples (i.e., patches) belonging to the same pathological tissue in the low-rank subspace and simultaneously push apart those from different latent subspaces. At the feature aggregation stage, we introduce an iterative low-rank attention MIL (ILRA-MIL) model to aggregate features with low-rank learnable latent vectors to model global interactions among all instances. We highlight the importance of instance correlation modeling but refrain from directly using the transformer encoder considering the $O(n^2)$ complexity. ILRA-MIL with LRC pre-trained features achieves strong empirical results across various benchmarks, including (i) 96.49\% AUC on the CAMELYON16 for binary metastasis classification, (ii) 97.63\% AUC on the TCGA-NSCLC for lung cancer subtyping, and (iii) 0.6562 kappa on the large-scale PANDA dataset for prostate cancer classification. The code is available at https://github.com/jinxixiang/low_rank_wsi.
Causal Balancing for Domain Generalization
Authors: Xinyi Wang,Michael Saxon,Jiachen Li,Hongyang Zhang,Kun Zhang,William Yang Wang

While machine learning models rapidly advance the state-of-the-art on various real-world tasks, out-of-domain (OOD) generalization remains a challenging problem given the vulnerability of these models to spurious correlations. We propose a balanced mini-batch sampling strategy to transform a biased data distribution into a spurious-free balanced distribution, based on the invariance of the underlying causal mechanisms for the data generation process. We argue that the Bayes optimal classifiers trained on such balanced distribution are minimax optimal across a diverse enough environment space. We also provide an identifiability guarantee of the latent variable model of the proposed data generation process, when utilizing enough train environments. Experiments are conducted on DomainBed, demonstrating empirically that our method obtains the best performance across 20 baselines reported on the benchmark.
Towards Addressing Label Skews in One-Shot Federated Learning
Authors: Yiqun Diao,Qinbin Li,Bingsheng He

Federated learning (FL) has been a popular research area, where multiple clients collaboratively train a model without sharing their local raw data. Among existing FL solutions, one-shot FL is a promising and challenging direction, where the clients conduct FL training with a single communication round. However, while label skew is a common real-world scenario where some clients may have few or no data of some classes, existing one-shot FL approaches that conduct voting on the local models are not able to produce effective global models. Due to the limited number of classes in each party, the local models misclassify the data from unseen classes into seen classes, which leads to very ineffective global models from voting. To address the label skew issue in one-shot FL, we propose a novel approach named FedOV which generates diverse outliers and introduces them as an additional unknown class in local training to improve the voting performance. Specifically, based on open-set recognition, we propose novel outlier generation approaches by corrupting the original features and further develop adversarial learning to enhance the outliers. Our extensive experiments show that FedOV can significantly improve the test accuracy compared to state-of-the-art approaches in various label skew settings.
Breaking Correlation Shift via Conditional Invariant Regularizer
Authors: Mingyang Yi,Ruoyu Wang,Jiacheng Sun,Zhenguo Li,Zhi-Ming Ma

Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attentions. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the models that are conditionally independent of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls the OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV. Under mild assumptions, our training objective can be formulated as a nonconvex-concave mini-max problem. An algorithm with a provable convergence rate is proposed to solve the problem. Extensive empirical results verify our algorithm's efficacy in improving OOD generalization.
Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case
Authors: Runzhong Wang,Li Shen,Yiting Chen,Xiaokang Yang,Dacheng Tao,Junchi Yan

One-shot non-autoregressive neural networks, different from RL-based ones, have been actively adopted for solving combinatorial optimization (CO) problems, which can be trained by the objective score in a self-supervised manner. Such methods have shown their superiority in efficiency (e.g. by parallelization) and potential for tackling predictive CO problems for decision-making under uncertainty. While the discrete constraints often become a bottleneck for gradient-based neural solvers, as currently handled in three typical ways: 1) adding a soft penalty in the objective, where a bounded violation of the constraints cannot be guaranteed, being critical to many constraint-sensitive scenarios; 2) perturbing the input to generate an approximate gradient in a black-box manner, though the constraints are exactly obeyed while the approximate gradients can hurt the performance on the objective score; 3) a compromise by developing soft algorithms whereby the output of neural networks obeys a relaxed constraint, and there can still occur an arbitrary degree of constraint-violation. Towards the ultimate goal of establishing a general framework for neural CO solver with the ability to control an arbitrary-small degree of constraint violation, in this paper, we focus on a more achievable and common setting: the cardinality constraints, which in fact can be readily encoded by a differentiable optimal transport (OT) layer. Based on this observation, we propose OT-based cardinality constraint encoding for end-to-end CO problem learning with two variants: Sinkhorn and Gumbel-Sinkhorn, whereby their violation of the constraints can be exactly characterized and bounded by our theoretical results. On synthetic and real-world CO problem instances, our methods surpass the state-of-the-art CO network and are comparable to (if not superior to) the commercial solver Gurobi. In particular, we further showcase a case study of applying our approach to the predictive portfolio optimization task on real-world asset price data, improving the Sharpe ratio from 1.1 to 2.0 of a strong LSTM+Gurobi baseline under the classic predict-then-optimize paradigm.
Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference
Authors: Yun-Chen Lo,Tse-Kuang Lee,Ren-Shuo Liu

In this paper, we propose Block and Subword-Scaling Floating-Point (BSFP), a non-uniform quantization scheme for the skewed and non-uniform distribution of weight vectors in neural networks. By quantizing each weight vector as the superposition of multiple subword vectors (in two's complement) with scaling factors (in Low-bit Floating-Point, LBFP), BSFP can effectively fit the distribution of weight vectors while maintaining high computation efficiency. Furthermore, we present a grid search-based MSE-optimal quantization flow and a scaled serial processing engine to complete the quantization pipeline and the infrastructure. 

The experimental results on the ImageNet classification task show that our proposed method outperforms state-of-the-art Microsoft Floating Point (MSFP) by up to 20.56% top-1 accuracy at the same weight precision and reduces up to 10.3% model size. Furthermore, BSFP outperforms MSFP by up to 2.0$\times$ computing throughput and up to 5.3$\times$ energy efficiency under the same silicon area budget.
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
Authors: Rundong Luo,Yifei Wang,Yisen Wang

Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervised AT (sup-AT) and self-supervised AT (self-AT) remains significant. Motivated by this observation, we revisit existing self-AT methods and discover an inherent dilemma that affects self-AT robustness: either strong or weak data augmentations are harmful to self-AT, and a medium strength is insufficient to bridge the gap. To resolve this dilemma, we propose a simple remedy named DYNACL (Dynamic Adversarial Contrastive Learning). In particular, we propose an augmentation schedule that gradually anneals from a strong augmentation to a weak one to benefit from both extreme cases. Besides, we adopt a fast post-processing stage for adapting it to downstream tasks. Through extensive experiments, we show that DYNACL can improve state-of-the-art self-AT robustness by 8.84% under Auto-Attack on the CIFAR-10 dataset, and can even outperform vanilla supervised adversarial training for the first time. Our code is available at \url{https://github.com/PKU-ML/DYNACL}.
Semi-supervised Community Detection via Structural Similarity Metrics
Authors: Yicong Jiang,Tracy Ke

Motivated by the interests of social network analysis and network-based recommendation systems, we consider a semi-supervised community detection problem, where the goal is to estimate the community label of a new node by leveraging on the network structure and partially observed community labels of existing nodes. 
We model the network with a degree-corrected stochastic block model, which allows for severe degree heterogeneity and potentially non-assortative communities. 
We propose a fast algorithm that computes a `structural similarity metric' between the new node and each of the $K$ communities, aggregating information in labeled and unlabeled data. The estimated label of the new node is equal to the value of $k$  that maximizes this similarity metric. Our method is computationally fast and compares favorably with existing semi-supervised algorithms on numerical performance. In theory, we derive explicit bounds for the misclassification error and show the efficiency of our method by comparing it with an ideal classifier. To our best knowledge, our results provide the first semi-supervised community detection algorithm with theoretical guarantees.
DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Authors: Tiange Xiang,Mahmut Yurt,Ali B Syed,Kawin Setsompop,Akshay Chaudhari

Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising Diffusion Models for Denoising Diffusion MRI (DDM^2), a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our three-stage framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation. During inference, we represent input noisy measurements as a sample from an intermediate posterior distribution within the diffusion Markov chain. We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM^2 demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics.
Multivariate Time-series Imputation with Disentangled Temporal Representations
Authors: SHUAI LIU,Xiucheng Li,Gao Cong,Yile Chen,YUE JIANG

Multivariate time series often faces the problem of missing value. Many time series imputation methods have been developed in the literature. However, these methods all rely on an entangled representation to model dynamics of time series, which may fail to fully exploit the multiple factors (e.g., periodic patterns) contained in the time series. Moreover, the entangled representation usually has no semantic meaning, and thus they often lack interpretability. In addition, many recent models are proposed to deal with the whole time series to capture cross-channel correlations and identify temporal dynamics, but they are not scalable to large-scale datasets. Different from existing approaches, we propose TIDER, a novel matrix factorization-based method with disentangled temporal representations that account for multiple factors, namely trend, seasonality, and local bias, to model complex dynamics. The learned disentanglement makes the imputation process more reliable and offers explainability for imputation results. Moreover, TIDER is scalable to large datasets. Empirical results show that our method not only outperforms existing approaches by notable margins on three real-world datasets, but also scales well to large datasets on which existing deep learning based methods struggle. Disentanglement validation experiments further demonstrate the robustness of our model in obtaining accurate and explainable disentangled components.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Authors: Zonghan Yang,Xiaoyuan Yi,Peng Li,Yang Liu,Xing Xie

Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful contents in training corpora, typically toxic language and social biases, raising severe moral issues. Prior works on ethical NLG tackle detoxifying and debiasing separately, which is problematic since we find debiased models still exhibit toxicity while detoxified ones even exacerbate biases. To address such a challenge, we propose the first unified framework of detoxifying and debiasing called UDDIA, which jointly formalizes these two problems as rectifying the output space. We theoretically interpret our framework as learning a text distribution mixing weighted attributes. Besides, UDDIA conducts adaptive optimization of only a few parameters during decoding based on a parameter-efficient tuning schema without any training data. This leads to minimal generation quality loss and improved rectification performance with acceptable computational cost. Experimental results demonstrate that compared to several strong baselines, UDDIA achieves debiasing and detoxifying simultaneously and better balances efficiency and effectiveness, taking a further step towards practical ethical NLG.
Automating Nearest Neighbor Search Configuration with Constrained Optimization
Authors: Philip Sun,Ruiqi Guo,Sanjiv Kumar

The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications. A number of techniques have been developed for ANN search that are efficient, accurate, and scalable. However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set. Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm. This is becoming an increasingly unrealistic demand as ANN search grows in popularity. To tackle this obstacle to ANN adoption, this work proposes a constrained optimization-based approach to tuning quantization-based ANN algorithms. Our technique takes just a desired search cost or recall as input, and then generates tunings that, empirically, are very close to the speed-recall Pareto frontier and give leading performance on standard benchmarks.