Papers Read on AI

Recently, deep learning based approaches have achieved great improvements in image matting. However, most of them require a user supplied trimap as an auxiliary input, which limits the matting applications in the real world. Our method applies a high-resolution detail branch (HRDB) that extracts fine-grained details of the foreground with keeping feature resolution unchanged.

2022: Guowei Chen, Yi Liu, Jian Wang, Juncai Peng, Yuying Hao, Lutao Chu, Shiyu Tang, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang, Xiaoguang Hu, Dianhai Yu

https://arxiv.org/pdf/2204.09433v1.pdf

Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks. However, existing methods such as BERT model a single document, and do not capture dependencies or knowledge that span across documents. In this work, we propose LinkBERT, an LM pretraining method that leverages links between documents, e.g., hyperlinks.

2022: Michihiro Yasunaga, J. Leskovec, Percy Liang

Ranked #1 on Text Classification on BLURB

https://arxiv.org/pdf/2203.15827v1.pdf

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required.

2017: Eliya Nachmani, Elad Marciano, Loren Lugosch, W. Gross, D. Burshtein, Yair Be’ery

Deep learning, Belief propagation, Algorithm, Artificial neural network, Recurrent neural network, Quantization (signal processing), Tanner graph, BCH code, Software propagation, Yet another, Signal-to-noise ratio, Linear code, Network architecture, Parity bit, Block code, End-to-end principle, Iteration, Maxima and minima, Random neural network, Computational complexity theory, Linear programming relaxation

https://arxiv.org/pdf/1706.07043v2.pdf

We demonstrate instant training of neural graphics primitives on a single GPU for multiple tasks. In Gigapixel image we represent a gigapixel image by a neural network. SDF learns a signed distance function in 3D space whose zero level-set represents a 2D surface. Neural radiance caching (NRC) [Müller et al. 2021] employs a neural network that is trained in real-time to cache costly lighting calculations. Lastly, NeRF uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching. In all tasks, our encoding and its efficient implementation provide clear benefits: rapid training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyper parameters across all tasks and only vary the hash table size which trades off quality and performance.

2022: T. Müller, Alex Evans, Christoph Schied, A. Keller

https://arxiv.org/pdf/2201.05989v1.pdf

We propose an efficient framework, called Simple Swap (SimSwap), aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face. We overcome the above defects in the following two ways. First, we present the ID Injection Module (IIM) which transfers the identity information of the source face into the target face at feature level. By using this module, we extend the architecture of an identity-specific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art methods.

2020: Renwang Chen, Xuanhong Chen, Bingbing Ni, Yanhao Ge

Ranked #2 on Face Swapping on FaceForensics++

https://arxiv.org/pdf/2106.06340v1.pdf

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers.

2022: Tianheng Cheng, Xinggang Wang, Shaoyu Chen, Wenqiang Zhang, Q. Zhang, Chang Huang, Zhaoxiang Zhang, Wenyu Liu

Ranked #1 on Real-time Instance Segmentation on MSCOCO

https://arxiv.org/pdf/2203.12827v1.pdf

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks.

2022: Junnan Li, Dongxu Li, Caiming Xiong, S. Hoi

Ranked #1 on Image Captioning on nocaps-val-out-domain

https://arxiv.org/pdf/2201.12086v2.pdf

Quantum Neural Network (QNN) is drawing increasing research interest thanks to its potential to achieve quantum advantage on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable QNN learning, the training process needs to be offloaded to real quantum machines instead of using exponential cost classical simulators. One common approach to obtain QNN gradients is parameter shift whose cost scales linearly with the number of qubits. We present On-chip QNN, the first experimental demonstration of practical on-chip QNN training with parameter shift.

2022: Hanrui Wang, Zi-Chen Li, Jiaqi Gu, Yongshan Ding, D. Pan, Song Han

https://arxiv.org/pdf/2202.13239v1.pdf

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.

2020: Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Chris Wang

https://arxiv.org/pdf/2011.09607v2.pdf

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.

2020: Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Chris Wang

https://arxiv.org/pdf/2011.09607v2.pdf