{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/batch-normalization/papers/38","list_of":"/method/batch-normalization","method":"Batch Normalization","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":38,"pages_in_order":63,"rows_per_page":100,"rows":[3701,3800],"of":6287,"counts":{"archive_papers_tagged":6287,"with_a_code_link":2771,"where_syntology_ran_a_sample":742,"not_listed_spam_title":0,"listed":6287,"listed_where_code_ran":742,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":627,"every_run_a_failure_of_syntologys_instrument":115,"listed_with_a_run_with_no_instrument_failure":627,"listed_every_run_a_failure_of_syntologys_instrument":115,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/batch-normalization","prev":"/method/batch-normalization/papers/37","next":"/method/batch-normalization/papers/39","papers":[{"paper":"/paper/densenet-for-breast-tumor-classification-in","slug":"densenet-for-breast-tumor-classification-in","title":"DenseNet for Breast Tumor Classification in Mammographic Images","date":"2021-01-24","arxiv_id":"2101.09637","n_code_links":2,"syntology":null},{"paper":"/paper/towards-robust-visual-information-extraction","slug":"towards-robust-visual-information-extraction","title":"Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution","date":"2021-01-24","arxiv_id":"2102.06732","n_code_links":1,"syntology":null},{"paper":null,"slug":"arabic-aspect-based-sentiment-analysis-using","title":"Arabic aspect based sentiment analysis using bidirectional GRU based models","date":"2021-01-23","arxiv_id":"2101.10539","n_code_links":0,"syntology":null},{"paper":"/paper/cm-nas-rethinking-cross-modality-neural","slug":"cm-nas-rethinking-cross-modality-neural","title":"CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification","date":"2021-01-21","arxiv_id":"2101.08467","n_code_links":1,"syntology":{"ran":3,"of":8,"n_ran_checked":3,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":{"repos":["jdai-cv/cm-nas"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/daf-re-a-challenging-crowd-sourced-large","slug":"daf-re-a-challenging-crowd-sourced-large","title":"DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition","date":"2021-01-21","arxiv_id":"2101.08674","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["arkel23/animesion"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/exponential-moving-average-normalization-for","slug":"exponential-moving-average-normalization-for","title":"Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning","date":"2021-01-21","arxiv_id":"2101.08482","n_code_links":1,"syntology":null},{"paper":null,"slug":"generative-zero-shot-network-quantization","title":"Generative Zero-shot Network Quantization","date":"2021-01-21","arxiv_id":"2101.08430","n_code_links":0,"syntology":null},{"paper":"/paper/ghostsr-learning-ghost-features-for-efficient","slug":"ghostsr-learning-ghost-features-for-efficient","title":"GhostSR: Learning Ghost Features for Efficient Image Super-Resolution","date":"2021-01-21","arxiv_id":"2101.08525","n_code_links":4,"syntology":null},{"paper":"/paper/ikshana-a-theory-of-human-scene-understanding","slug":"ikshana-a-theory-of-human-scene-understanding","title":"The Ikshana Hypothesis of Human Scene Understanding","date":"2021-01-21","arxiv_id":"2101.10837","n_code_links":2,"syntology":null},{"paper":null,"slug":"motif-identification-using-cnn-based-pairwise","title":"Motif Identification using CNN-based Pairwise Subsequence Alignment Score Prediction","date":"2021-01-21","arxiv_id":"2101.08385","n_code_links":0,"syntology":null},{"paper":null,"slug":"fooling-thermal-infrared-pedestrian-detectors","title":"Fooling thermal infrared pedestrian detectors in real world using small bulbs","date":"2021-01-20","arxiv_id":"2101.08154","n_code_links":0,"syntology":null},{"paper":null,"slug":"splitsr-an-end-to-end-approach-to-super","title":"SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices","date":"2021-01-20","arxiv_id":"2101.07996","n_code_links":0,"syntology":null},{"paper":"/paper/deep-convolutional-autoencoders-for","slug":"deep-convolutional-autoencoders-for","title":"Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain","date":"2021-01-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-models-for-calculation-of","title":"Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly","date":"2021-01-19","arxiv_id":"2101.07606","n_code_links":0,"syntology":null},{"paper":"/paper/momentum-2-teacher-momentum-teacher-with","slug":"momentum-2-teacher-momentum-teacher-with","title":"Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning","date":"2021-01-19","arxiv_id":"2101.07525","n_code_links":1,"syntology":null},{"paper":null,"slug":"source-free-domain-adaptation-via-1","title":"Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics","date":"2021-01-19","arxiv_id":"2101.10842","n_code_links":0,"syntology":null},{"paper":null,"slug":"variance-based-samples-weighting-for","title":"Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning","date":"2021-01-19","arxiv_id":"2101.07561","n_code_links":0,"syntology":null},{"paper":null,"slug":"tlu-net-a-deep-learning-approach-for","title":"TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection","date":"2021-01-18","arxiv_id":"2101.06915","n_code_links":0,"syntology":null},{"paper":null,"slug":"cost-efficient-online-hyperparameter","title":"Cost-Efficient Online Hyperparameter Optimization","date":"2021-01-17","arxiv_id":"2101.06590","n_code_links":0,"syntology":null},{"paper":null,"slug":"separable-batch-normalization-for-robust","title":"Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training","date":"2021-01-17","arxiv_id":"2101.06663","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-cycle-consistent-synthesis-of","title":"Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation","date":"2021-01-16","arxiv_id":"2101.06468","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-representation-learning-from-3","title":"Self-Supervised Representation Learning from Flow Equivariance","date":"2021-01-16","arxiv_id":"2101.06553","n_code_links":0,"syntology":null},{"paper":"/paper/deep-dual-resolution-networks-for-real-time","slug":"deep-dual-resolution-networks-for-real-time","title":"Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes","date":"2021-01-15","arxiv_id":"2101.06085","n_code_links":8,"syntology":null},{"paper":null,"slug":"dynamic-normalization","title":"Dynamic Normalization","date":"2021-01-15","arxiv_id":"2101.06073","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multiple-classifier-approach-for","title":"A Multiple Classifier Approach for Concatenate-Designed Neural Networks","date":"2021-01-14","arxiv_id":"2101.05457","n_code_links":0,"syntology":null},{"paper":"/paper/fabricnet-a-fiber-recognition-architecture","slug":"fabricnet-a-fiber-recognition-architecture","title":"FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets","date":"2021-01-14","arxiv_id":"2101.05564","n_code_links":1,"syntology":null},{"paper":"/paper/gan-inversion-a-survey","slug":"gan-inversion-a-survey","title":"GAN Inversion: A Survey","date":"2021-01-14","arxiv_id":"2101.05278","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-prediction-of-alzheimer-s","title":"Deep learning based prediction of Alzheimer's disease from magnetic resonance images","date":"2021-01-13","arxiv_id":"2101.04961","n_code_links":0,"syntology":null},{"paper":"/paper/neural-sequence-to-grid-module-for-learning","slug":"neural-sequence-to-grid-module-for-learning","title":"Neural Sequence-to-grid Module for Learning Symbolic Rules","date":"2021-01-13","arxiv_id":"2101.04921","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-creating-a-deployable-grasp-type","title":"Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand","date":"2021-01-13","arxiv_id":"2101.05357","n_code_links":0,"syntology":null},{"paper":null,"slug":"whispered-and-lombard-neural-speech-synthesis","title":"Whispered and Lombard Neural Speech Synthesis","date":"2021-01-13","arxiv_id":"2101.05313","n_code_links":0,"syntology":null},{"paper":"/paper/repvgg-making-vgg-style-convnets-great-again","slug":"repvgg-making-vgg-style-convnets-great-again","title":"RepVGG: Making VGG-style ConvNets Great Again","date":"2021-01-11","arxiv_id":"2101.03697","n_code_links":25,"syntology":{"ran":13,"of":16,"n_ran_checked":8,"n_instrument":5,"unverified":3,"pointer_only":6,"phrase":"13 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["DingXiaoH/RepVGG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"accuracy-and-architecture-studies-of-residual","title":"Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations","date":"2021-01-10","arxiv_id":"2101.03583","n_code_links":0,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-with-function","slug":"deep-reinforcement-learning-with-function","title":"Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies","date":"2021-01-09","arxiv_id":"2101.03418","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-adversarial-fake-images-on-face","title":"Exploring Adversarial Fake Images on Face Manifold","date":"2021-01-09","arxiv_id":"2101.03272","n_code_links":0,"syntology":null},{"paper":null,"slug":"bn-invariant-sharpness-regularizes-the","title":"BN-invariant sharpness regularizes the training model to better generalization","date":"2021-01-08","arxiv_id":"2101.02944","n_code_links":0,"syntology":null},{"paper":null,"slug":"nvae-gan-based-approach-for-unsupervised-time","title":"NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection","date":"2021-01-08","arxiv_id":"2101.02908","n_code_links":0,"syntology":null},{"paper":null,"slug":"residual-networks-classify-inputs-based-on-1","title":"Residual networks classify inputs based on their neural transient dynamics","date":"2021-01-08","arxiv_id":"2101.03009","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthetic-glacier-sar-image-generation-from","title":"Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm","date":"2021-01-08","arxiv_id":"2101.03252","n_code_links":0,"syntology":null},{"paper":"/paper/end-2-end-covid-19-detection-from-breath","slug":"end-2-end-covid-19-detection-from-breath","title":"End-2-End COVID-19 Detection from Breath & Cough Audio","date":"2021-01-07","arxiv_id":"2102.08359","n_code_links":2,"syntology":null},{"paper":"/paper/facial-expression-recognition-in-the-wild-via","slug":"facial-expression-recognition-in-the-wild-via","title":"Facial Expression Recognition in the Wild via Deep Attentive Center Loss","date":"2021-01-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"more-reliable-ai-solution-breast-ultrasound","title":"More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination","date":"2021-01-07","arxiv_id":"2101.02639","n_code_links":0,"syntology":null},{"paper":null,"slug":"vhs-to-hdtv-video-translation-using-multi","title":"VHS to HDTV Video Translation using Multi-task Adversarial Learning","date":"2021-01-07","arxiv_id":"2101.02384","n_code_links":0,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic","title":"CAP-Context-Aware-Pruning-for-Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic-1","title":"CAP: Context-Aware Pruning for Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/industrial-image-anomaly-localization-based","slug":"industrial-image-anomaly-localization-based","title":"Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature","date":"2021-01-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"isetauto-detecting-vehicles-with-depth-and","title":"ISETAuto: Detecting vehicles with depth and radiance information","date":"2021-01-06","arxiv_id":"2101.01843","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automatic-system-to-monitor-the-physical","title":"An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic","date":"2021-01-05","arxiv_id":"2101.01373","n_code_links":0,"syntology":null},{"paper":null,"slug":"contextual-colorization-and-denoising-for-low","title":"Contextual colorization and denoising for low-light ultra high resolution sequences","date":"2021-01-05","arxiv_id":"2101.01597","n_code_links":0,"syntology":null},{"paper":"/paper/local-memory-attention-for-fast-video","slug":"local-memory-attention-for-fast-video","title":"Local Memory Attention for Fast Video Semantic Segmentation","date":"2021-01-05","arxiv_id":"2101.01715","n_code_links":1,"syntology":null},{"paper":null,"slug":"cy-chaotic-yolo-for-user-intended-image","title":"Cy: Chaotic yolo for user intended image encryption and sharing in social media","date":"2021-01-04","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"low-light-image-enhancement-via-global-and","title":"Low Light Image Enhancement via Global and Local Context Modeling","date":"2021-01-04","arxiv_id":"2101.00850","n_code_links":0,"syntology":null},{"paper":"/paper/one-shot-model-for-the-prediction-of-covid-19","slug":"one-shot-model-for-the-prediction-of-covid-19","title":"One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features","date":"2021-01-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/few-shot-image-classification-just-use-a","slug":"few-shot-image-classification-just-use-a","title":"Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier","date":"2021-01-03","arxiv_id":"2101.00562","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["arjish/PreTrainedFullLibrary_FewShot"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/regnet-self-regulated-network-for-image","slug":"regnet-self-regulated-network-for-image","title":"RegNet: Self-Regulated Network for Image Classification","date":"2021-01-03","arxiv_id":"2101.00590","n_code_links":14,"syntology":null},{"paper":null,"slug":"a-block-minifloat-representation-for-training","title":"A Block Minifloat Representation for Training Deep Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-geometric-analysis-of-deep-generative-image","slug":"a-geometric-analysis-of-deep-generative-image","title":"A Geometric Analysis of Deep Generative Image Models and Its Applications","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-half-space-stochastic-projected-gradient","title":"A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-masking-towards-understanding","title":"Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"aggregation-with-feature-detection","title":"Aggregation With Feature Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"alfa-adversarial-feature-augmentation-for","title":"ALFA: Adversarial Feature Augmentation for Enhanced Image Recognition","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-artificial-intelligence-system-for-1","title":"An Artificial Intelligence System for Combined Fruit Detection and Georeferencing, Using RTK-Based Perspective Projection in Drone Imagery","date":"2021-01-01","arxiv_id":"2101.00339","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-music-production-using-generative","title":"Automatic Music Production Using Generative Adversarial Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-covid-19-diagnosis-prognosis-with","title":"Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/bidirectional-variational-inference-for-non","slug":"bidirectional-variational-inference-for-non","title":"Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"boosting-certified-robustness-of-deep","title":"Boosting Certified Robustness of Deep Networks via a Compositional Architecture","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"consistent-instance-classification-for","title":"Consistent Instance Classification for Unsupervised Representation Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"constructing-multiple-high-quality-deep","title":"Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"diet-snn-a-low-latency-spiking-neural-network","title":"DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"differentiable-dynamic-wirings-for-neural","title":"Differentiable Dynamic Wirings for Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evidence-against-implicitly-recurrent","title":"Evidence against implicitly recurrent computations in residual neural networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-training-of-contrastive-learning-with","title":"Fast Training of Contrastive Learning with Intermediate Contrastive Loss","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-neural-architecture","title":"Generative Adversarial Neural Architecture Search with Importance Sampling","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-time-series-modeling-with-fourier","title":"Generative Time-series Modeling with Fourier Flows","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-low-precision-network-quantization","title":"Improving Low-Precision Network Quantization via Bin Regularization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"inhibition-augmented-convnets","title":"Inhibition-augmented ConvNets","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"invariant-batch-normalization-for-multi","title":"Invariant Batch Normalization for Multi-source Domain Generalization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-visual-representation-from-human","title":"Learning Visual Representation from Human Interactions","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"making-coherence-out-of-nothing-at-all-1","title":"Making Coherence Out of Nothing At All: Measuring Evolution of Gradient Alignment","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"metanorm-learning-to-normalize-few-shot","title":"MetaNorm: Learning to Normalize Few-Shot Batches Across Domains","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"moco-pretraining-improves-representations-and","title":"MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/model-free-energy-distance-for-pruning-dnns","slug":"model-free-energy-distance-for-pruning-dnns","title":"Model-Free Energy Distance for Pruning DNNs","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-grid-back-projection-networks","title":"Multi-Grid Back-Projection Networks","date":"2021-01-01","arxiv_id":"2101.00150","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-representation-ensemble-in-few-shot","title":"Multi-Representation Ensemble in Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nahas-neural-architecture-and-hardware","title":"NAHAS: Neural Architecture and Hardware Accelerator Search","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/naturalistic-physical-adversarial-patch-for","slug":"naturalistic-physical-adversarial-patch-for","title":"Naturalistic Physical Adversarial Patch for Object Detectors","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-networks-preserve-invertibility-across","title":"Neural Networks Preserve Invertibility Across Iterations: A Possible Source of Implicit Data Augmentation","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"not-so-big-gan-generating-high-fidelity","title":"not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"phew-paths-with-higher-edge-weights-give","title":"PHEW: Paths with Higher Edge-Weights give ''winning tickets'' without training data","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"playing-nondeterministic-games-through","title":"Playing Nondeterministic Games through Planning with a Learned Model","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"polarnet-learning-to-optimize-polar-keypoints","title":"PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"practical-locally-private-federated-learning","title":"Practical Locally Private Federated Learning with Communication Efficiency","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"recycling-sub-optimial-hyperparameter","title":"Recycling sub-optimial Hyperparameter Optimization models to generate efficient Ensemble Deep Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"redefining-self-normalization-property","title":"Redefining Self-Normalization Property","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sad-saliency-adversarial-defense-without","title":"SAD: Saliency Adversarial Defense without Adversarial Training","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sedona-search-for-decoupled-neural-networks","slug":"sedona-search-for-decoupled-neural-networks","title":"SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"the-3tconv-an-intrinsic-approach-to","title":"The 3TConv: An Intrinsic Approach to Explainable 3D CNNs","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"variance-based-sample-weighting-for","title":"Variance Based Sample Weighting for Supervised Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-transformers-where-do-transformers","title":"Visual Transformers: Where Do Transformers Really Belong in Vision Models?","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weights-having-stable-signs-are-important","title":"Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"20d52eee925521569930abaf250b796cbf736e2ff224db5f1028212685520eb5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}