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Found 17 papers, 4 papers with code
On Mitigating Hard Clusters for Face Clustering
Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images.
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects.
EventFormer: AU Event Transformer for Facial Action Unit Event Detection
Facial action units (AUs) play an indispensable role in human emotion analysis.
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation in HEVC/H.265 through Frank-Wolfe Policy Optimization
More recently, the dual-critic design is proposed to update the actor network by alternating the rate and distortion critics.
Optimized Separable Convolution: Yet Another Efficient Convolution Operator
In this research, we propose a novel and principled operator called optimized separable convolution by optimal design for the internal number of groups and kernel sizes for general separable convolutions can achieve the complexity of O(C^{\frac{3}{2}}K).
Self-paced Resistance Learning against Overfitting on Noisy Labels
They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
A Dual-Critic Reinforcement Learning Framework for Frame-level Bit Allocation in HEVC/H.265
This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H. 265.
Systolic Computing on GPUs for Productive Performance
We propose a language and compiler to productively build high-performance {\it software systolic arrays} that run on GPUs.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks.
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource.
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner.
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.
Component-Based Distributed Framework for Coherent and Real-Time Video Dehazing
Traditional dehazing techniques, as a well studied topic in image processing, are now widely used to eliminate the haze effects from individual images.
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick.
