{"list":"other","total":87,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"2210.09461","title":"Token Merging: Your ViT But Faster","created":"2022-10-17","cited_by_count":1021},{"id":"2106.02034","title":"DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification","created":"2021-06-03","cited_by_count":864},{"id":"2106.05974","title":"Scaling Vision with Sparse Mixture of Experts","created":"2021-06-10","cited_by_count":747},{"id":"1709.01686","title":"BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks","created":"2017-09-06","cited_by_count":596},{"id":"1905.08094","title":"Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation","created":"2019-05-17","cited_by_count":497},{"id":"2112.07658","title":"AdaViT: Adaptive Tokens for Efficient Vision Transformer","created":"2021-12-14","cited_by_count":362},{"id":"1711.09485","title":"SkipNet: Learning Dynamic Routing in Convolutional Networks","created":"2017-11-26","cited_by_count":332},{"id":"2111.15668","title":"AdaViT: Adaptive Vision Transformers for Efficient Image Recognition","created":"2021-11-30","cited_by_count":228},{"id":"2303.17604","title":"Token Merging for Fast Stable Diffusion","created":"2023-03-30","cited_by_count":224},{"id":"2308.06767","title":"A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations","created":"2023-08-13","cited_by_count":202},{"id":"1711.08393","title":"BlockDrop: Dynamic Inference Paths in Residual Networks","created":"2017-11-22","cited_by_count":189},{"id":"2106.04533","title":"Chasing Sparsity in Vision Transformers: An End-to-End Exploration","created":"2021-06-08","cited_by_count":183},{"id":"2111.15667","title":"Adaptive Token Sampling For Efficient Vision Transformers","created":"2021-11-30","cited_by_count":183},{"id":"2108.01390","title":"Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer","created":"2021-08-03","cited_by_count":177},{"id":"1910.05316","title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","created":"2019-10-04","cited_by_count":152},{"id":"1711.11503","title":"Convolutional Networks with Adaptive Inference Graphs","created":"2017-11-30","cited_by_count":148},{"id":"2003.07326","title":"Resolution Adaptive Networks for Efficient Inference","created":"2020-03-16","cited_by_count":146},{"id":"2105.15075","title":"Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition","created":"2021-05-31","cited_by_count":144},{"id":"2312.01026","title":"Token Fusion: Bridging the Gap between Token Pruning and Token Merging","created":"2023-12-02","cited_by_count":125},{"id":"1612.02297","title":"Spatially Adaptive Computation Time for Residual Networks","created":"2016-12-07","cited_by_count":118},{"id":"2103.04505","title":"Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges","created":"2021-03-08","cited_by_count":115},{"id":"2008.06402","title":"SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud","created":"2020-08-14","cited_by_count":112},{"id":"2304.10716","title":"Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers","created":"2023-04-21","cited_by_count":81},{"id":"2003.10401","title":"Learning Dynamic Routing for Semantic Segmentation","created":"2020-03-23","cited_by_count":79},{"id":"2105.03245","title":"Adaptive Focus for Efficient Video Recognition","created":"2021-05-07","cited_by_count":77},{"id":"2004.12814","title":"Why should we add early exits to neural networks?","created":"2020-04-27","cited_by_count":70},{"id":"1509.08971","title":"Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition","created":"2015-09-29","cited_by_count":67},{"id":"1704.06363","title":"Hard Mixtures of Experts for Large Scale Weakly Supervised Vision","created":"2017-04-20","cited_by_count":67},{"id":"1908.06294","title":"Improved Techniques for Training Adaptive Deep Networks","created":"2019-08-17","cited_by_count":66},{"id":"2308.01045","title":"Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation","created":"2023-08-02","cited_by_count":62},{"id":"2303.17605","title":"SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer","created":"2023-03-30","cited_by_count":60},{"id":"1701.00299","title":"Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution","created":"2017-01-02","cited_by_count":59},{"id":"1604.06119","title":"Network of Experts for Large-Scale Image Categorization","created":"2016-04-20","cited_by_count":54},{"id":"2302.01392","title":"Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion","created":"2023-02-02","cited_by_count":54},{"id":"2306.07050","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","created":"2023-06-12","cited_by_count":50},{"id":"2211.11315","title":"Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers","created":"2022-11-21","cited_by_count":47},{"id":"1811.11205","title":"You Look Twice: GaterNet for Dynamic Filter Selection in CNNs","created":"2018-11-27","cited_by_count":46},{"id":"2209.08310","title":"Learning to Weight Samples for Dynamic Early-exiting Networks","created":"2022-09-17","cited_by_count":44},{"id":"2106.05409","title":"Zero Time Waste: Recycling Predictions in Early Exit Neural Networks","created":"2021-06-09","cited_by_count":39},{"id":"2404.01882","title":"Scene Adaptive Sparse Transformer for Event-based Object Detection","created":"2024-04-02","cited_by_count":39},{"id":"2112.14238","title":"AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition","created":"2021-12-28","cited_by_count":37},{"id":"2108.10394","title":"Dynamic Network Quantization for Efficient Video Inference","created":"2021-08-23","cited_by_count":36},{"id":"1707.05733","title":"Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments","created":"2017-07-18","cited_by_count":34},{"id":"2306.11248","title":"Dynamic Perceiver for Efficient Visual Recognition","created":"2023-06-20","cited_by_count":33},{"id":"1907.04523","title":"Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference","created":"2019-07-10","cited_by_count":31},{"id":"2008.03997","title":"HAPI: Hardware-Aware Progressive Inference","created":"2020-08-10","cited_by_count":30},{"id":"2308.10110","title":"Robust Mixture-of-Expert Training for Convolutional Neural Networks","created":"2023-08-19","cited_by_count":30},{"id":"2006.16581","title":"Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images","created":"2020-06-30","cited_by_count":28},{"id":"2012.11655","title":"Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation","created":"2020-12-21","cited_by_count":28},{"id":"2105.09121","title":"Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead","created":"2021-05-19","cited_by_count":26},{"id":"2407.19985","title":"Mixture of Nested Experts: Adaptive Processing of Visual Tokens","created":"2024-07-29","cited_by_count":25},{"id":"2202.07101","title":"A Survey on Dynamic Neural Networks for Natural Language Processing","created":"2022-02-15","cited_by_count":24},{"id":"2206.04685","title":"Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference","created":"2022-06-09","cited_by_count":24},{"id":"2001.00705","title":"Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference","created":"2020-01-03","cited_by_count":23},{"id":"2306.02095","title":"Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers","created":"2023-06-03","cited_by_count":22},{"id":"2011.02422","title":"Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing","created":"2020-10-27","cited_by_count":21},{"id":"2008.09662","title":"Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations","created":"2020-08-21","cited_by_count":20},{"id":"2304.08451","title":"Efficient Video Action Detection with Token Dropout and Context Refinement","created":"2023-04-17","cited_by_count":20},{"id":"2307.02321","title":"MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers","created":"2023-07-05","cited_by_count":20},{"id":"2302.06359","title":"Fixing Overconfidence in Dynamic Neural Networks","created":"2023-02-13","cited_by_count":19},{"id":"2311.03035","title":"GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation","created":"2023-11-06","cited_by_count":19},{"id":"2407.03197","title":"DyFADet: Dynamic Feature Aggregation for Temporal Action Detection","created":"2024-07-03","cited_by_count":19},{"id":"2106.03527","title":"Multi-Exit Semantic Segmentation Networks","created":"2021-06-07","cited_by_count":18},{"id":"2209.13465","title":"AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition","created":"2022-09-27","cited_by_count":18},{"id":"2201.06644","title":"HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception","created":"2022-01-17","cited_by_count":16},{"id":"2303.08010","title":"Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models","created":"2023-03-14","cited_by_count":16},{"id":"2202.11330","title":"EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception","created":"2022-02-23","cited_by_count":13},{"id":"2402.00893","title":"MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts","created":"2024-01-31","cited_by_count":12},{"id":"2403.07965","title":"Conditional computation in neural networks: principles and research trends","created":"2024-03-12","cited_by_count":10},{"id":"2405.02330","title":"Adaptive Semantic Token Selection for AI-native Goal-oriented Communications","created":"2024-04-25","cited_by_count":10},{"id":"2011.12025","title":"SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation","created":"2020-11-24","cited_by_count":9},{"id":"2401.13330","title":"NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks","created":"2024-01-24","cited_by_count":9},{"id":"2304.06724","title":"GradMDM: Adversarial Attack on Dynamic Networks","created":"2023-04-01","cited_by_count":8},{"id":"2210.15621","title":"Class Based Thresholding in Early Exit Semantic Segmentation Networks","created":"2022-10-27","cited_by_count":5},{"id":"2310.05654","title":"No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling","created":"2023-10-09","cited_by_count":5},{"id":"1807.11249","title":"Modular Sensor Fusion for Semantic Segmentation","created":"2018-07-30","cited_by_count":3},{"id":"2101.08717","title":"Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?","created":"2021-01-21","cited_by_count":3},{"id":"2204.13347","title":"A Closer Look at Branch Classifiers of Multi-exit Architectures","created":"2022-04-28","cited_by_count":3},{"id":"2207.08865","title":"Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems","created":"2022-07-18","cited_by_count":3},{"id":"2304.08400","title":"ATHEENA: A Toolflow for Hardware Early-Exit Network Automation","created":"2023-04-17","cited_by_count":3},{"id":"2304.10306","title":"FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits","created":"2023-04-20","cited_by_count":3},{"id":"2205.03974","title":"SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection","created":"2022-05-08","cited_by_count":2},{"id":"2303.08215","title":"Stress Detection using Context-Aware Sensor Fusion from Wearable Devices","created":"2023-03-14","cited_by_count":2}],"next_cursor":null}