We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arbitrary privacy measurements that maps from the distortion to a real value. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning. Such adaptive and fine-grained protection can improve the effectiveness of privacy-preserved federated learning. Theoretically, we show that gap between the utility loss of the protection hyperparameter output by our algorithm and that of the optimal protection hyperparameter is sub-linear in the total number of iterations. The sublinearity of our algorithm indicates that the average gap between the performance of our algorithm and that of the optimal performance goes to zero when the number of iterations goes to infinity. Further, we provide the convergence rate of our proposed algorithm. We conduct empirical results on benchmark datasets to verify that our method achieves better utility than the baseline methods under the same privacy budget.
Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabeled videos. Typically, state-of-the-art SSVH methods consider these two points in a two-stage training pipeline, where they firstly train an auxiliary network by instance-wise mask-and-predict tasks and secondly train a hashing model to preserve the pseudo-neighborhood structure transferred from the auxiliary network. This consecutive training strategy is inflexible and also unnecessary. In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames. Particularly, we find that a higher masking ratio helps video understanding. Besides, we fully exploit the similarity relationship between videos by maximizing agreement between two augmented views of a video, which contributes to more discriminative and robust hash codes. Extensive experiments on three large-scale video datasets (i.e., FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art results. Code is available at https://github.com/huangmozhi9527/ConMH.
* This work is accepted by the AAAI 2023. 9 pages, 6 figures, 6 tables
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decompression with a single NVIDIA Tesla V100 GPU, 10 times faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.
Deep neural networks notoriously suffer from dataset biases which are detrimental to model robustness, generalization and fairness. In this work, we propose a two-stage debiasing scheme to combat against the intractable unknown biases. Starting by analyzing the factors of the presence of biased models, we design a novel learning objective which cannot be reached by relying on biases alone. Specifically, debiased models are achieved with the proposed Gradient Alignment (GA) which dynamically balances the contributions of bias-aligned and bias-conflicting samples (refer to samples with/without bias cues respectively) throughout the whole training process, enforcing models to exploit intrinsic cues to make fair decisions. While in real-world scenarios, the potential biases are extremely hard to discover and prohibitively expensive to label manually. We further propose an automatic bias-conflicting sample mining method by peer-picking and training ensemble without prior knowledge of bias information. Experiments conducted on multiple datasets in various settings demonstrate the effectiveness and robustness of our proposed scheme, which successfully alleviates the negative impact of unknown biases and achieves state-of-the-art performance.
Compared with tedious per-pixel mask annotating, it is much easier to annotate data by clicks, which costs only several seconds for an image. However, applying clicks to learn video semantic segmentation model has not been explored before. In this work, we propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick, for saving laborious annotating effort by segmenting an instance of the semantic class with only a single click. Since detailed semantic information is not captured by clicks, directly training with click labels leads to poor segmentation predictions. To mitigate this problem, we design a novel memory flow knowledge distillation strategy to exploit temporal information (named memory flow) in abundant unlabeled video frames, by distilling the neighboring predictions to the target frame via estimated motion. Moreover, we adopt vanilla knowledge distillation for model compression. In this case, WeClick learns compact video semantic segmentation models with the low-cost click annotations during the training phase yet achieves real-time and accurate models during the inference period. Experimental results on Cityscapes and Camvid show that WeClick outperforms the state-of-the-art methods, increases performance by 10.24% mIoU than baseline, and achieves real-time execution.
Object detectors are typically learned based on fully-annotated training data with fixed pre-defined categories. However, not all possible categories of interest can be known beforehand, as classes are often required to be increased progressively in many realistic applications. In such scenario, only the original training set annotated with the old classes and some new training data labeled with the new classes are available. In this paper, we aim at leaning a strong unified detector that can handle all categories based on the limited datasets without extra manual labor. Vanilla joint training without considering label ambiguity leads to heavy biases and poor performance due to the incomplete annotations. To avoid such situation, we propose a practical framework which focuses on three aspects: better base model, better unlabeled ground-truth mining strategy and better retraining method with pseudo annotations. First, a conflict-free loss is proposed to obtain a usable base detector. Second, we employ Monte Carlo Dropout to calculate the localization confidence, combined with the classification confidence, to mine more accurate bounding boxes. Third, we explore several strategies for making better use of pseudo annotations during retraining to achieve more powerful detectors. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of our framework for category-extended object detectors.
Instead of conducting manual factor construction based on traditional and behavioural finance analysis, academic researchers and quantitative investment managers have leveraged Genetic Programming (GP) as an automatic feature construction tool in recent years, which builds reverse polish mathematical expressions from trading data into new factors. However, with the development of deep learning, more powerful feature extraction tools are available. This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures. The experiment results show that NNAFC can construct more informative and diversified factors than GP, to effectively enrich the current factor pool. For the current market, both fully connected and recurrent neural network structures are better at extracting information from financial time series than convolution neural network structures. Moreover, new factors constructed by NNAFC can always improve the return, Sharpe ratio, and the max draw-down of a multi-factor quantitative investment strategy due to their introducing more information and diversification to the existing factor pool.
* Accepted by the Journal, Quantitative Finance. 21 Pages
This work studied the score-based black-box adversarial attack problem, where only a continuous score is returned for each query, while the structure and parameters of the attacked model are unknown. A promising approach to solve this problem is evolution strategies (ES), which introduces a search distribution to sample perturbations that are likely to be adversarial. Gaussian distribution is widely adopted as the search distribution in the standard ES algorithm. However, it may not be flexible enough to capture the diverse distributions of adversarial perturbations around different benign examples. In this work, we propose to transform the Gaussian-distributed variable to another space through a conditional flow-based model, to enhance the capability and flexibility of capturing the intrinsic distribution of adversarial perturbations conditioned on the benign example. Besides, to further enhance the query efficiency, we propose to pre-train the conditional flow model based on some white-box surrogate models, utilizing the transferability of adversarial perturbations across different models, which has been widely observed in the literature of adversarial examples. Consequently, the proposed method could take advantage of both query-based and transfer-based attack methods, to achieve satisfying attack performance on both effectiveness and efficiency. Extensive experiments of attacking four target models on CIFAR-10 and Tiny-ImageNet verify the superior performance of the proposed method to state-of-the-art methods.