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Found 18 papers, 3 papers with code
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
To establish a unified evaluation framework for video generation tasks, our benchmark includes 11 metrics spanning four dimensions to assess algorithm performance.
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
To model the deformation more accurately, we propose to initialize an estimated 3D clothed human in the canonical space, as it is easier for deformation fields to learn from the clothed human than from SMPL.
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames.
Quality at the Tail of Machine Learning Inference
The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time.
Heterologous Normalization
Specifically, it calculates the mean like Batch Normalization to maintain the advantage of Batch Normalization.
Shift-and-Balance Attention
The attention branch is gated using the Sigmoid function and multiplied by the feature map's trunk branch.
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.
Image Classification
Performance
FLBench: A Benchmark Suite for Federated Learning
The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data island, while keeping their data secure and private.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN).
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains.
AIBench Scenario: Scenario-distilling AI Benchmarking
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
Extended Batch Normalization
To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed.
Ranked #61 on
Image Classification
on STL-10
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN.
