wanyu/IteraTeR_v2 huggingface dataset:
Original:  Additionally , we design a feature enhancement module to learn more features of small-dim targets  . After that,  we adopt a decoder with the U-Net-like skip connection operation to  get the detection result  . Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods  .
Revised:  Moreover , we design a feature enhancement module to learn more features of small-dim targets . After that, we adopt a decoder with the U-Net-like skip connection operation to get the detection result . Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods .
Edit intent: fluency
Original:  Additionally , we design a feature enhancement module to learn more features of small-dim targets  . After that,  we adopt a decoder with the U-Net-like skip connection operation to  get the detection result  . Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods  .
Revised:  Additionally , we design a feature enhancement module to learn discriminative features of small-dim targets . After that, we adopt a decoder with the U-Net-like skip connection operation to get the detection result . Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods .
Edit intent: clarity
Original:  While they outperform earlier convolutional networks, the complexity quadratic to N is one of the major drawbacks when using traditional self-attention algorithms.
Revised:  Although they outperform prior works, they require heavy computational resources on a scale that is quadratic to N is one of the major drawbacks when using traditional self-attention algorithms.
Edit intent: clarity
Original:  While they outperform earlier convolutional networks, the complexity quadratic to N is one of the major drawbacks when using traditional self-attention algorithms.
Revised:  While they outperform earlier convolutional networks, the complexity quadratic to N . This is a major drawback of the traditional self-attention algorithms.
Edit intent: coherence
Original:  Here we propose the UFO-ViT( Unit Force Operated Vision Trnasformer), novel method to reduce the computations of self-attention by eliminating some non-linearity.
Revised:  Here we propose the Unit Force Operated Vision Trnasformer), novel method to reduce the computations of self-attention by eliminating some non-linearity.
Edit intent: clarity
Original:  The proposed models outperform most transformer-based models on image classification and dense prediction tasks through most capacity regime .
Revised:  The proposed models outperform most transformer-based models on image classification and dense prediction tasks on most capacity regimes .
Edit intent: clarity
Original:  The loss function used to train the deep neural networks highly impact the performance of both balanced and imbalanced tasks . Currently, the cross-entropy loss remains the de-facto loss function for balanced and imbalanced classification tasks .
Revised:  The loss function used to train the deep neural networks . Currently, the cross-entropy loss remains the de-facto loss function for balanced and imbalanced classification tasks .
Edit intent: clarity
Original:  The loss function used to train the deep neural networks highly impact the performance of both balanced and imbalanced tasks . Currently, the cross-entropy loss remains the de-facto loss function for balanced and imbalanced classification tasks .
Revised:  The loss function used to train the deep neural networks highly impact the performance of both balanced and imbalanced tasks . Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers .
Edit intent: clarity
Original:  This loss  , however, asserts equal learning to all classes, leading to the classification of most samples as the majority normal class.
Revised:  This loss function , however, asserts equal learning to all classes, leading to the classification of most samples as the majority normal class.
Edit intent: clarity
Original:  This loss  , however, asserts equal learning to all classes, leading to the classification of most samples as the majority normal class.
Revised:  This loss , however, asserts equal learning from all classes, leading to the classification of most samples as the majority normal class.
Edit intent: clarity
Original:  We also construct prediction-level and model-level ensembles of the models that are trained with various loss functions to improve classification performance.
Revised:  We also construct prediction-level and model-level ensembles , respectively, to improve classification performance.
Edit intent: clarity
Original:  We performed localization studies to interpret model behavior to ensure that the individual models and their ensembles precisely learned the regions of interest showing disease manifestationsto classify the chest X-rays to their respective categories .
Revised:  We performed localization studies to interpret model behavior to ensure that the individual models and ensembles learned meaningful features and highlighted disease manifestations .
Edit intent: clarity
Original: PreCICE is a free/open-source coupling library.
Revised: preCICE is a free/open-source coupling library.
Edit intent: fluency
Original:  20  century formalizations of learning theory -- which precipitated revolutions in artificial intelligence -- focus primarily on in-distribution learning, that is, learning under the assumption that the training data are sampled from the same distribution as the evaluation distribution.
Revised:  20 ^{st century formalizations of learning theory -- which precipitated revolutions in artificial intelligence -- focus primarily on in-distribution learning, that is, learning under the assumption that the training data are sampled from the same distribution as the evaluation distribution.
Edit intent: fluency
Original:  We also substantiate the superiority of the proposed algorithm on training better NNs.
Revised:  We also substantiate the superiority of the proposed algorithm on training NNs.
Edit intent: clarity
Original:  However, we lack any principled way to evaluate how good a ``proxy domain'' is, specifically in terms of how useful it is in helping us achieve our end objective of building an agent that performs well in the target domain.
Revised:  However, we lack any principled way to evaluate how good a "proxy domain" is, specifically in terms of how useful it is in helping us achieve our end objective of building an agent that performs well in the target domain.
Edit intent: fluency
Original:  Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world due to lack of dataset standardization, sparse annotation tools, and insufficient formatting and aggregation of existing, expertly curated imagery for use by data scientists.
Revised:  Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of dataset standardization, sparse annotation tools, and insufficient formatting and aggregation of existing, expertly curated imagery for use by data scientists.
Edit intent: clarity
Original:  Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world due to lack of dataset standardization, sparse annotation tools, and insufficient formatting and aggregation of existing, expertly curated imagery for use by data scientists.
Revised:  Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world due to lack of data set standardization, insufficient formatting, and aggregation of existing, expertly curated imagery for use by data scientists.
Edit intent: clarity
Original:  To address this need, we have built FathomNet, a public platform that makes use of existing (and future) , expertly curated data.
Revised:  To address this need, we have built FathomNet, a public platform that makes use of existing , expertly curated data.
Edit intent: clarity
Original:  Initial efforts have leveraged MBARI's Video Annotation and Reference System and annotated deep sea video database, which has more than 7M annotations, 1M framegrabs , and 5k terms in the knowledgebase, with additional contributions by National Geographic Society (NGS) and NOAA's Office of Ocean Exploration and Research.
Revised:  Initial efforts have leveraged MBARI's Video Annotation and Reference System and annotated deep sea video database, which has more than 7M annotations, 1M frame grabs , and 5k terms in the knowledgebase, with additional contributions by National Geographic Society (NGS) and NOAA's Office of Ocean Exploration and Research.
Edit intent: fluency
Original:  We will demonstrate how machine learning models trained on FathomNet data can be applied across different institutional video data, (e.g., NGS'
Revised:  We demonstrate how machine learning models trained on FathomNet data can be applied across different institutional video data, (e.g., NGS'
Edit intent: coherence
Original:  Deep Sea Camera System and NOAA's ROV Deep Discoverer), and enable automated acquisition and tracking of midwater animals using MBARI's ROV MiniROV . As FathomNet continues to develop and incorporate more image data from other oceanographic community members, this effort will enable scientists, explorers, policymakers, storytellers, and the public to understand and care for our ocean.
Revised:  Deep Sea Camera System and NOAA's ROV Deep Discoverer), and enable automated acquisition and tracking of midwater animals using a remotely operated vehicle . As FathomNet continues to develop and incorporate more image data from other oceanographic community members, this effort will enable scientists, explorers, policymakers, storytellers, and the public to understand and care for our ocean.
Edit intent: clarity
Original:  The representations learned from CNN's operated on hyperspherical manifold led to insightful outcomes in face recognition, face identification  and other supervised tasks.
Revised:  The representations learned from CNN's operated on hyperspherical manifold led to insightful outcomes in face recognition, face identification , and other supervised tasks.
Edit intent: fluency
Original:  A broad range of activation functions is developed with hypersphere intuition which performs superior to softmax in euclidean space.
Revised:  A broad range of activation functions were developed with hypersphere intuition which performs superior to softmax in euclidean space.
Edit intent: clarity
Original:  Secondly, proving both theoretically and practically that decision boundaries constructed on hypersphere using stereographic projection obliges the learning of neural networks.
Revised:  Secondly, proving theoretically and practically that decision boundaries constructed on hypersphere using stereographic projection obliges the learning of neural networks.
Edit intent: clarity
Original:  Experiments have proved that applying stereographic projection on existing state-of-the-art angular margin objective functions led to improve performance for standard image classification data sets (CIFAR-10,100).
Revised:  Experiments have demonstrated that applying stereographic projection on existing state-of-the-art angular margin objective functions led to improve performance for standard image classification data sets (CIFAR-10,100).
Edit intent: clarity
Original:  Experiments have proved that applying stereographic projection on existing state-of-the-art angular margin objective functions led to improve performance for standard image classification data sets (CIFAR-10,100).
Revised:  Experiments have proved that applying stereographic projection on existing state-of-the-art angular margin objective functions improved performance for standard image classification data sets (CIFAR-10,100).
Edit intent: clarity
Original:   The code is publicly available at: URL
Revised:  The code is publicly available URL
Edit intent: clarity
Original:  The set of discrete normal distributions form an exponential family with cumulant function related to the Riemann holomorphic theta function.
Revised:  The set of discrete normal distributions form an exponential family with cumulant function related to the Riemann theta function.
Edit intent: clarity
Original:  In this work , we present formula for several common statistical divergences between discrete normal distributions including the Kullback-Leibler divergence.
Revised:  In this paper , we present formula for several common statistical divergences between discrete normal distributions including the Kullback-Leibler divergence.
Edit intent: clarity
Original:  In this work , we present formula for several common statistical divergences between discrete normal distributions including the Kullback-Leibler divergence.
Revised:  In this work , we present several formula for common statistical divergences between discrete normal distributions including the Kullback-Leibler divergence.
Edit intent: fluency
Original:  In particular, we describe an efficient approximation technique for approximating the Kullback-Leibler divergence between discrete normal distributions via the  \gamma-divergences.
Revised:  In particular, we describe an efficient approximation technique for calculating the Kullback-Leibler divergence between discrete normal distributions via the \gamma-divergences.
Edit intent: clarity
Original:  The warped mesh will be better refined with our proposed Elastic Instance Normalization , which is a conditional normalization layer and can help to generate high-quality meshes.
Revised:  The warped mesh will be better refined with our proposed Elastic Instance Normalization , which is a conditional normalization layer and can help to generate high-quality meshes.
Edit intent: fluency
Original: AI explainability seeks to increase the transparency of models, making them more trustworthy in the process.
Revised: AI explainability improves the transparency of models, making them more trustworthy in the process.
Edit intent: clarity
Original:  The need for transparency has been recently motivated by the emergence of deep learning models, which are particularly obscure by nature .
Revised:  The need for transparency has been recently motivated by the emergence of deep learning models, which are obscure by nature .
Edit intent: clarity
Original:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Revised:  Multiple feature attribution methods have been proposed with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Edit intent: clarity
Original:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Revised:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Edit intent: clarity
Original:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Revised:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual cues. However, no metrics have been established so far to assess and select these methods exist .
Edit intent: clarity
Original:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods exist .
Revised:  Multiple feature attribution methods have been proposed in the literature with the purpose of explaining a DL model's behavior using visual queues, but no standardized metrics to assess or select these methods objectively .
Edit intent: clarity
Original:  In this paper we propose a novel evaluation metric -- the Focus -- designed to quantify the faithfulness of explanations provided by feature attribution methods, such as LRP or GradCAM . First, we show the robustness of the metric through randomization experiments, and then use Focus to evaluate and compare three popular explainability techniques using multiple architectures and datasets.
Revised:  In this paper we propose a novel evaluation metric -- the Focus -- designed to quantify the faithfulness of explanations provided by feature attribution methods, such as LRP or GradCAM . First, we show the robustness of the approach through a set of randomization experiments. Then we use Focus to evaluate and compare three popular explainability techniques using multiple architectures and datasets.
Edit intent: clarity
Original:  In this paper we propose a novel evaluation metric -- the Focus -- designed to quantify the faithfulness of explanations provided by feature attribution methods, such as LRP or GradCAM . First, we show the robustness of the metric through randomization experiments, and then use Focus to evaluate and compare three popular explainability techniques using multiple architectures and datasets.
Revised:  In this paper we propose a novel evaluation metric -- the Focus -- designed to quantify the faithfulness of explanations provided by feature attribution methods, such as LRP or GradCAM . First, we show the robustness of the metric through randomization experiments, and then use Focus to evaluate and compare three popular explainability techniques across several CNN architectures and classification datasets.
Edit intent: clarity
Original:  We synthetically add camera motion to a newly acquired dataset of camera motion free da Vinci surgery image sequences through the introduction of a novel homography generation algorithm.
Revised:  We synthetically add camera motion to a newly acquired dataset of camera motion free da Vinci surgery image sequences through a novel homography generation algorithm.
Edit intent: coherence
Original:  We further demonstrate the goodness of SsAG by accurately and efficiently answering the queries related to the graph structure and attribute information using the summary only.
Revised:  We further demonstrate the goodness of SSAG by accurately and efficiently answering the queries related to the graph structure and attribute information using the summary only.
Edit intent: fluency
Original:  This bias makes the model suffer from weak generalization ability, leading to worse performance on downstream tasks such as action recognition.
Revised:  Such bias makes the model suffer from weak generalization ability, leading to worse performance on downstream tasks such as action recognition.
Edit intent: fluency
Original:  To alleviate such bias, we propose Foreground-background Merging (FAME) to deliberately compose the  foreground region of the selected video onto the  background of others.
Revised:  To alleviate such bias, we propose Foreground-background Merginga (FAME) to deliberately compose the foreground region of the selected video onto the background of others.
Edit intent: fluency
Original:  By leveraging the semantic consistency between the original clips and the fused ones, the model focuses more on the foreground motion pattern and is thus more robust to the background context .
Revised:  By leveraging the semantic consistency between the original clips and the fused ones, the model focuses more on the motion patterns and is debiased from the background shortcut .
Edit intent: clarity
Original:  When integrated with MoCo, FAME reaches 84.8\% and 53.5\% accuracy on UCF101 and HMDB51, respectively, achieving the state-of-the-art performance .
Revised:  When integrated with MoCo, FAME reaches 84.8\% and 53.5\% accuracy on UCF101 , HMDB51, respectively, achieving the state-of-the-art performance .
Edit intent: fluency
Original:  This result is achieved by considering non-Maxwell-Boltzmann distributions of the symbols  .
Revised:  This is achieved by considering non-Maxwell-Boltzmann distributions of the symbols .
Edit intent: clarity
Original: Artificial Intelligence (AI), especially Neural Network (NN ), has become increasingly popular.
Revised: Artificial Intelligence (AI), especially Neural Networks (NNs ), has become increasingly popular.
Edit intent: fluency
Original:  AIive enables users to manipulate the parameters of NN with virtual hands and provides auditory feedback for the real-time values of loss, accuracy, and hyperparameters.
Revised:  AIive enables users to manipulate the parameters of NNs with virtual hands and provides auditory feedback for the real-time values of loss, accuracy, and hyperparameters.
Edit intent: fluency
Original:  We then extend that result to graphs where each neighbourhood has bounded density, which strengthens results due to Vu, and  Davies, P., Kang  and Sereni.
Revised:  We then extend that result to graphs where each neighbourhood has bounded density, which strengthens results due to Vu, and Davies, P., Kang , and Sereni.
Edit intent: fluency
Original: This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge , a challenge in which a three fingered robot must carry a cube along specified goal trajectories.
Revised: This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge ; a challenge in which a three fingered robot must carry a cube along specified goal trajectories.
Edit intent: fluency
Original:  A sparse  goal-based reward is employed in conjunction with Hindsight Experience Replay to teach the control policy to move the cube to the desired x and y coordinates.
Revised:  A sparse , goal-based reward is employed in conjunction with Hindsight Experience Replay to teach the control policy to move the cube to the desired x and y coordinates.
Edit intent: fluency
Original:  Recent analytical transferability metrics are mainly designed for image classification  , and currently there is no specific investigation for the transferability estimation of semantic segmentation task, which is an essential problem in autonomous driving, medical image analysis, etc.
Revised:  Recent analytical transferability metrics are mainly designed for image classification problem , and currently there is no specific investigation for the transferability estimation of semantic segmentation task, which is an essential problem in autonomous driving, medical image analysis, etc.
Edit intent: coherence
Original:  The subsets are downloaded to associated devices for updating.
Revised:  The subnets are downloaded to associated devices for updating.
Edit intent: clarity
Original:  Thereby, FdeDrop reduces both the communication overhead and devices' computation loads compared with the conventional FL while outperforming the latter in the case of overfitting and also the FL scheme with uniform dropout (i.e., identical subsets ).
Revised:  Thereby, FedDrop reduces both the communication overhead and devices' computation loads compared with the conventional FL while outperforming the latter in the case of overfitting and also the FL scheme with uniform dropout (i.e., identical subsets ).
Edit intent: fluency
Original:  Thereby, FdeDrop reduces both the communication overhead and devices' computation loads compared with the conventional FL while outperforming the latter in the case of overfitting and also the FL scheme with uniform dropout (i.e., identical subsets ).
Revised:  Thereby, FdeDrop reduces both the communication overhead and devices' computation loads compared with the conventional FL while outperforming the latter in the case of overfitting and also the FL scheme with uniform dropout (i.e., identical subnets ).
Edit intent: clarity