19 research outputs found
Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent
An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-party OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Replay-based methods in class-incremental learning (CIL) have attained
remarkable success, as replaying the exemplars of old classes can significantly
mitigate catastrophic forgetting. Despite their effectiveness, the inherent
memory restrictions of CIL result in saving a limited number of exemplars with
poor diversity, leading to data imbalance and overfitting issues. In this
paper, we introduce a novel exemplar super-compression and regeneration method,
ESCORT, which substantially increases the quantity and enhances the diversity
of exemplars. Rather than storing past images, we compress images into visual
and textual prompts, e.g., edge maps and class tags, and save the prompts
instead, reducing the memory usage of each exemplar to 1/24 of the original
size. In subsequent learning phases, diverse high-resolution exemplars are
generated from the prompts by a pre-trained diffusion model, e.g., ControlNet.
To minimize the domain gap between generated exemplars and real images, we
propose partial compression and diffusion-based data augmentation, allowing us
to utilize an off-the-shelf diffusion model without fine-tuning it on the
target dataset. Therefore, the same diffusion model can be downloaded whenever
it is needed, incurring no memory consumption. Comprehensive experiments
demonstrate that our method significantly improves model performance across
multiple CIL benchmarks, e.g., 5.0 percentage points higher than the previous
state-of-the-art on 10-phase Caltech-256 dataset.Comment: Code: https://github.com/KerryDRX/ESCOR
Semi-supervised Medical Image Segmentation through Dual-task Consistency
Deep learning-based semi-supervised learning (SSL) algorithms have led to
promising results in medical images segmentation and can alleviate doctors'
expensive annotations by leveraging unlabeled data. However, most of the
existing SSL algorithms in literature tend to regularize the model training by
perturbing networks and/or data. Observing that multi/dual-task learning
attends to various levels of information which have inherent prediction
perturbation, we ask the question in this work: can we explicitly build
task-level regularization rather than implicitly constructing networks- and/or
data-level perturbation-and-transformation for SSL? To answer this question, we
propose a novel dual-task-consistency semi-supervised framework for the first
time. Concretely, we use a dual-task deep network that jointly predicts a
pixel-wise segmentation map and a geometry-aware level set representation of
the target. The level set representation is converted to an approximated
segmentation map through a differentiable task transform layer. Simultaneously,
we introduce a dual-task consistency regularization between the level
set-derived segmentation maps and directly predicted segmentation maps for both
labeled and unlabeled data. Extensive experiments on two public datasets show
that our method can largely improve the performance by incorporating the
unlabeled data. Meanwhile, our framework outperforms the state-of-the-art
semi-supervised medical image segmentation methods. Code is available at:
https://github.com/Luoxd1996/DTCComment: 9 pages, 4 figure
Label-Free Liver Tumor Segmentation
We demonstrate that AI models can accurately segment liver tumors without the
need for manual annotation by using synthetic tumors in CT scans. Our synthetic
tumors have two intriguing advantages: (I) realistic in shape and texture,
which even medical professionals can confuse with real tumors; (II) effective
for training AI models, which can perform liver tumor segmentation similarly to
the model trained on real tumors -- this result is exciting because no existing
work, using synthetic tumors only, has thus far reached a similar or even close
performance to real tumors. This result also implies that manual efforts for
annotating tumors voxel by voxel (which took years to create) can be
significantly reduced in the future. Moreover, our synthetic tumors can
automatically generate many examples of small (or even tiny) synthetic tumors
and have the potential to improve the success rate of detecting small liver
tumors, which is critical for detecting the early stages of cancer. In addition
to enriching the training data, our synthesizing strategy also enables us to
rigorously assess the AI robustness.Comment: CVPR 202
Compositor: Bottom-up Clustering and Compositing for Robust Part and Object Segmentation
In this work, we present a robust approach for joint part and object
segmentation. Specifically, we reformulate object and part segmentation as an
optimization problem and build a hierarchical feature representation including
pixel, part, and object-level embeddings to solve it in a bottom-up clustering
manner. Pixels are grouped into several clusters where the part-level
embeddings serve as cluster centers. Afterwards, object masks are obtained by
compositing the part proposals. This bottom-up interaction is shown to be
effective in integrating information from lower semantic levels to higher
semantic levels. Based on that, our novel approach Compositor produces part and
object segmentation masks simultaneously while improving the mask quality.
Compositor achieves state-of-the-art performance on PartImageNet and
Pascal-Part by outperforming previous methods by around 0.9% and 1.3% on
PartImageNet, 0.4% and 1.7% on Pascal-Part in terms of part and object mIoU and
demonstrates better robustness against occlusion by around 4.4% and 7.1% on
part and object respectively. Code will be available at
https://github.com/TACJu/Compositor
FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition
A neuromorphic vision sensors is a novel passive sensing modality and frameless sensors with several advantages over conventional cameras. Frame-based cameras have an average frame-rate of 30 fps, causing motion blur when capturing fast motion, e.g., hand gesture. Rather than wastefully sending entire images at a fixed frame rate, neuromorphic vision sensors only transmit the local pixel-level changes induced by the movement in a scene when they occur. This leads to advantageous characteristics, including low energy consumption, high dynamic range, a sparse event stream and low response latency. In this study, a novel representation learning method was proposed: Fixed Length Gists Representation (FLGR) learning for event-based gesture recognition. Previous methods accumulate events into video frames in a time duration (e.g., 30 ms) to make the accumulated image-level representation. However, the accumulated-frame-based representation waives the friendly event-driven paradigm of neuromorphic vision sensor. New representation are urgently needed to fill the gap in non-accumulated-frame-based representation and exploit the further capabilities of neuromorphic vision. The proposed FLGR is a sequence learned from mixture density autoencoder and preserves the nature of event-based data better. FLGR has a data format of fixed length, and it is easy to feed to sequence classifier. Moreover, an RNN-HMM hybrid was proposed to address the continuous gesture recognition problem. Recurrent neural network (RNN) was applied for FLGR sequence classification while hidden Markov model (HMM) is employed for localizing the candidate gesture and improving the result in a continuous sequence. A neuromorphic continuous hand gestures dataset (Neuro ConGD Dataset) was developed with 17 hand gestures classes for the community of the neuromorphic research. Hopefully, FLGR can inspire the study on the event-based highly efficient, high-speed, and high-dynamic-range sequence classification tasks