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Jun 23, 2024 · Our goal is to reduce the training time of the ViT while maintaining competitive performance. Keywords. Vision Transformers, Training Time ...
Aug 5, 2024 · How integrating Batch Normalization in an encoder-only Transformer architecture can lead to reduced training time and inference time.
Missing: Reinforcement | Show results with:Reinforcement
Jul 13, 2023 · I have conducted experiments and examples on accelerating ViT (Vision Transformer) using methods such as TensorRT, FasterTransformer, and xFormers.
Missing: Reinforcement | Show results with:Reinforcement
Jan 27, 2024 · SkipViT is capable of effectively dropping 55% of the tokens while gaining 13.23% training throughput and maintaining classification accuracy.
Jan 27, 2024 · SkipViT is capable of effectively dropping 55% of the tokens while gaining more than 13% training throughput and maintaining classification accuracy.
Missing: Reinforcement | Show results with:Reinforcement
Apr 11, 2023 · Swin vision transformer showed better performance compared to the Performer, even though the improvements proposed in the Performer were not ...
Missing: Reinforcement | Show results with:Reinforcement
Jun 4, 2023 · This article introduces four tactics that can make vision transformer predict at a much faster speed by using tools such as ONNX, TensorRT and multi-threading.
In this work, we introduce FastViT, a hybrid vision transformer architecture that obtains the state-of-the-art latency-accuracy trade-off.
Missing: up Reinforcement
We introduce some algorithmic improvements to enable training a ViT model from scratch with limited hardware (1 GPU) and time (24 hours) resources.
Missing: Reinforcement | Show results with:Reinforcement
People also ask
Although these methods have achieved good performance with fewer Flops or parameters, many of them do not show significant wall-clock speedup against standard ...